Artificial neural network architectures based on synaptic connectivity graphs

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an artificial neural network architecture based on a synaptic connectivity graph. According to one aspect, there is provided a method comprising: obtaining a synaptic resolution image of at least a portion of a brain of a biological organism; processing the image to identify: (i) a plurality of neurons in the brain, and (ii) a plurality of synaptic connections between pairs of neurons in the brain; generating data defining a graph representing synaptic connectivity between the neurons in the brain; determining an artificial neural network architecture corresponding to the graph representing the synaptic connectivity between the neurons in the brain; and processing a network input using an artificial neural network having the artificial neural network architecture to generate a network output.

CROSS REFERENCE TO RELATED APPLICATION

This patent application is a continuation (and claims the benefit ofpriority under 35 USC 120) of U.S. Pat. Application Serial No.16/731,396, filed Dec. 31, 2019. The disclosure of the prior applicationis considered part of (and is incorporated by reference in) thedisclosure of this application.

BACKGROUND

This specification relates to processing data using machine learningmodels.

Machine learning models receive an input and generate an output, e.g., apredicted output, based on the received input. Some machine learningmodels are parametric models and generate the output based on thereceived input and on values of the parameters of the model.

Some machine learning models are deep models that employ multiple layersof computational units to generate an output for a received input. Forexample, a deep neural network is a deep machine learning model thatincludes an output layer and one or more hidden layers that each apply anon-linear transformation to a received input to generate an output.

SUMMARY

This specification describes systems implemented as computer programs onone or more computers in one or more locations for processing a synapticresolution image of the brain of a biological organism to generate asynaptic connectivity graph, and implementing an artificial neuralnetwork having an architecture specified by the synaptic connectivitygraph. A synaptic connectivity graph refers to a graph representing thestructure of synaptic connections between neurons in the brain of abiological organism, e.g., a fly.

For convenience, throughout this specification, a neural network havingan architecture specified by a synaptic connectivity graph may bereferred to as a “brain emulation” neural network. Identifying anartificial neural network as a “brain emulation” neural network isintended only to conveniently distinguish such neural networks fromother neural networks (e.g., with hand-engineered architectures), andshould not be interpreted as limiting the nature of the operations thatmay be performed by the neural network or otherwise implicitlycharacterizing the neural network.

According to a first aspect there is provided a method performed by oneor more data processing apparatus for training a student neural networkhaving a set of student neural network parameters. The method includesrepeatedly performing operations comprising processing a training inputusing the student neural network to generate an output for the traininginput. The student neural network output is processed using adiscriminative neural network to generate a discriminative score for thestudent neural network output. The discriminative neural network istrained to process a network input to generate a discriminative scorethat characterizes a prediction for whether the network input wasgenerated using: (i) the student neural network, or (ii) a brainemulation neural network having a set of brain emulation neural networkparameters. The brain emulation neural network has a neural networkarchitecture that is specified by a graph representing synapticconnectivity between neurons in a brain of a biological organism. Thegraph includes a set of nodes and edges, where each edge connects a pairof nodes, each node corresponds to a respective neuron in the brain ofthe biological organism, and each edge connecting a pair of nodes in thegraph corresponds to a synaptic connection between a pair of neurons inthe brain of the biological organism. The current values of the studentneural network parameters are adjusted using gradients of an objectivefunction that depends on the discriminative score for the student neuralnetwork output.

In some implementations, the student neural network is configured toprocess an input that includes image data, video data, audio data, odordata, point cloud data, magnetic field data, or a combination thereof,to generate an output that includes an embedding of the input.

In some implementations, the neural network architecture of the studentneural network is less complex than the neural network architecture ofthe brain emulation neural network.

In some implementations, adjusting the current values of the studentneural network parameters using gradients of an objective function thatdepends on the discriminative score for the student neural networkoutput encourages the student neural network to generate outputs thatare more likely to be misclassified by the discriminative neural networkas having been generated by the brain emulation neural network.

In some implementations, specifying the neural network architecture ofthe brain emulation neural network by the graph representing synapticconnectivity between neurons in the brain of the biological organismincludes mapping each node in the graph to a corresponding artificialneuron in the neural network architecture of the brain emulation neuralnetwork. Each edge in the graph is mapped to a connection between a pairof artificial neurons in the neural network architecture of the brainemulation neural network that correspond to the pair of nodes in thegraph that are connected by the edge.

In some implementations, the graph representing synaptic connectivitybetween neurons in the brain of the biological organism is generated byprocessing a synaptic resolution image of at least a portion of thebrain of the biological organism to identify: (i) a set of neurons inthe brain, and (ii) a set of synaptic connections between pairs ofneurons in the brain.

In some implementations, the synaptic resolution image of the brain ofthe biological organism is generated using electron microscopytechniques.

In some implementations, the graph represents synaptic connectivitybetween neurons in the brain of the biological organism that arepredicted to have a particular function in the brain of the biologicalorganism.

In some implementations, the particular function is a visual dataprocessing function, an audio data processing function, or an odor dataprocessing function.

In some implementations, values of the set of brain emulation neuralnetwork parameters are determined randomly prior to training of thestudent neural network and are not adjusted during the training of thestudent neural network.

In some implementations, the biological organism is an animal, e.g., afly.

According to a second aspect there is provided a method performed by oneor more data processing apparatus for training a student neural networkhaving a set of student neural network parameters. The method comprisesrepeatedly performing operations including processing a training inputusing the student neural network to generate a student neural networkoutput including a respective score for each of multiple classes. Thetraining input is processed using a brain emulation neural networkhaving a set of brain emulation neural network parameters to generate abrain emulation neural network output including a respective score foreach of the classes. The brain emulation neural network has a neuralnetwork architecture that is specified by a graph representing synapticconnectivity between neurons in a brain of a biological organism. Thegraph includes a set of nodes and edges, where each edge connects a pairof nodes, each node corresponds to a respective neuron in the brain ofthe biological organism, and each edge connecting a pair of nodes in thegraph corresponds to a synaptic connection between a pair of neurons inthe brain of the biological organism. The current values of the studentneural network parameters are adjusted using gradients of an objectivefunction that characterizes a similarity between: (i) the student neuralnetwork output for the training input, and (ii) the brain emulationneural network output for the training input.

In some implementations, adjusting the current values of the studentneural network parameters using gradients of the objective functionencourages the student neural network to generate student neural networkoutputs that match brain emulation neural network outputs generated bythe brain emulation neural network.

In some implementations, the student neural network is configured toprocess an input including image data, video data, audio data, odordata, point cloud data, magnetic field data, or a combination thereof.

In some implementations, the neural network architecture of the studentneural network is less complex than the neural network architecture ofthe brain emulation neural network.

In some implementations, specifying the neural network architecture ofthe brain emulation neural network by the graph representing synapticconnectivity between neurons in the brain of the biological organismincludes mapping each node in the graph to a corresponding artificialneuron in the neural network architecture of the brain emulation neuralnetwork. Each edge in the graph is mapped to a connection between a pairof artificial neurons in the neural network architecture of the brainemulation neural network that correspond to the pair of nodes in thegraph that are connected by the edge.

In some implementations, the graph representing synaptic connectivitybetween neurons in the brain of the biological organism is generated byprocessing a synaptic resolution image of at least a portion of thebrain of the biological organism to identify: (i) a set of neurons inthe brain, and (ii) a set of synaptic connections between pairs ofneurons in the brain.

In some implementations, the synaptic resolution image of the brain ofthe biological organism is generated using electron microscopytechniques.

In some implementations, the graph represents synaptic connectivitybetween neurons in the brain of the biological organism that arepredicted to have a particular function in the brain of the biologicalorganism.

In some implementations, the particular function is a visual dataprocessing function, an audio data processing function, or an odor dataprocessing function.

In some implementations, the values of the set of brain emulation neuralnetwork parameters are trained on a set of training data using machinelearning training techniques prior to training of the student neuralnetwork.

According to a third aspect, there is provided a method performed by oneor more data processing apparatus, the method including obtaining asynaptic resolution image of at least a portion of a brain of abiological organism. The image is processed to identify: (i) a set ofneurons in the brain, and (ii) a set of synaptic connections betweenpairs of neurons in the brain. Data defining a graph representingsynaptic connectivity between the neurons in the brain is generated,where the graph includes a set of nodes and edges, where each edgeconnects a pair of nodes. Each neuron in the brain is identified as arespective node in the graph. Each synaptic connection between a pair ofneurons in the brain is identified as an edge between a correspondingpair of nodes in the graph. An artificial neural network architecture isdetermined that corresponds to the graph representing the synapticconnectivity between the neurons in the brain. A network input isprocessed using an artificial neural network having the artificialneural network architecture to generate a network output.

In some implementations, determining an artificial neural networkarchitecture corresponding to the graph representing the synapticconnectivity between the neurons in the brain includes mapping each nodein the graph to a corresponding artificial neuron in the artificialneural network architecture. Each edge in the graph is mapped to aconnection between a pair of artificial neurons in the artificial neuralnetwork architecture that correspond to the pair of nodes in the graphthat are connected by the edge.

In some implementations, the method further includes processing theimage to identify a respective direction of each of the synapticconnections between pairs of neurons in the brain. Generating datadefining the graph further includes determining a direction of each edgein the graph based on the direction of the synaptic connectioncorresponding to the edge. Each connection between a pair of artificialneurons in the artificial neural network architecture has a directionspecified by the direction of the corresponding edge in the graph.

In some implementations, the method further includes processing theimage to determine a respective weight value for each of the synapticconnections between pairs of neurons in the brain. Generating datadefining the graph further includes determining a weight value for eachedge in the graph based on the weight value for the synaptic connectioncorresponding to the edge. Each connection between a pair of artificialneurons in the artificial neural network architecture has a weight valuespecified by the weight value of the corresponding edge in the graph.

In some implementations, processing a network input using an artificialneural network having the artificial neural network architecture togenerate a network output includes, for each of multiple givenartificial neurons of the artificial neural network, receivingartificial neuron inputs from other artificial neurons in the artificialneural network that are connected to the given artificial neuron byconnections directed towards the given artificial neuron. An artificialneuron output is generated based on the artificial neuron inputs. Theartificial neuron output is provided to other artificial neurons in theartificial neural network that are connected to the given artificialneuron by connections directed away from the given artificial neuron.

In some implementations, the method further includes training theartificial neural network having the artificial neural networkarchitecture using machine learning training techniques on a set oftraining data.

In some implementations, the network input includes image data.

In some implementations, the network output includes classification datathat specifies a respective score for each of multiple classes.

In some implementations, the synaptic resolution image of the brain ofthe biological organism is generated using electron microscopytechniques.

In some implementations, processing the image to identify: (i) a set ofneurons in the brain, and (ii) a set of synaptic connections betweenpairs of neurons in the brain, includes: identifying positions of theneurons in the image; and identifying the synaptic connections betweenpairs of neurons based on proximity of the positions of the neurons inthe image.

In some implementations, identifying positions of neurons in the imageincludes processing the image, features derived from the image, or both,using a machine learning model that is trained using supervised learningtechniques to identify positions of neurons in images.

In some implementations, identifying the synaptic connections betweenpairs of neurons based on proximity of the positions of the neurons inthe image includes, for one or more pairs of neurons including a firstneuron and a second neuron: determining: (i) a first tolerance region inthe image around the first neuron, and (ii) a second tolerance region inthe image around the second neuron; and determining that the firstneuron is connected by a synapse to the second neuron based on anoverlap between the first tolerance region and the second toleranceregion.

In some implementations, processing the image to determine a respectiveweight value for each of the synaptic connections between pairs ofneurons in the brain includes, for a synaptic connection between a firstneuron and a second neuron in the brain, determining the weight valuefor the synaptic connection between the first neuron and the secondneuron based on a proximity of the first neuron and the second neuron inthe image.

According to a fourth aspect, there is provided a method performed byone or more data processing apparatus, the method including obtainingdata defining a graph representing synaptic connectivity between neuronsin a brain of a biological organism. The graph includes a set of nodesand edges, where each edge connects a pair of nodes, each nodecorresponds to a respective neuron in the brain of the biologicalorganism, and each edge connecting a pair of nodes in the graphcorresponds to a synaptic connection between a pair of neurons in thebrain of the biological organism. The method includes determining, foreach node in the graph, a respective set of one or more node featurescharacterizing a structure of the graph relative to the node. The methodincludes identifying a sub-graph of the graph, including selecting aproper subset of the nodes in the graph for inclusion in the sub-graphbased on the node features of the nodes in the graph. The methodincludes determining an artificial neural network architecturecorresponding to the sub-graph of the graph.

In some implementations, for each node in the graph, the set of nodefeatures characterizing the structure of the graph relative to the nodeincludes one or more of: a node degree feature specifying a number ofother nodes that are connected to the node by an edge; a path lengthfeature specifying a length of a longest path in the graph starting fromthe node; or a neighborhood size feature specifying a number of othernodes that are connected to the node by a path in the graph having alength that is less than or equal to a threshold value.

In some implementations, obtaining data defining the graph representingsynaptic connectivity between neurons in the brain of the biologicalorganism includes obtaining data defining a weight value for each edgein the graph, where the weight value for each edge in the graphcharacterizes the corresponding synaptic connection in the brain of thebiological organism.

In some implementations, for each node in the graph, the set of nodefeatures characterizing the structure of the graph relative to the nodeare determined based at least in part on the weight values of edgesconnecting the node to other nodes in the graph.

In some implementations, the proper subset of the nodes in the graphselected for inclusion in the sub-graph are predicted to correspond toneurons having a particular function in the brain of the biologicalorganism.

In some implementations, the particular function is a visual dataprocessing function, and the method further includes providing anartificial neural network having the artificial neural networkarchitecture corresponding to the sub-graph of the graph for performingan image processing task.

In some implementations, the particular function is an audio dataprocessing function, and the method further includes providing anartificial neural network having the artificial neural networkarchitecture corresponding to the sub-graph of the graph for performingan audio data processing task.

In some implementations, the particular function is an odor dataprocessing function, and the method further includes providing anartificial neural network having the artificial neural networkarchitecture corresponding to the sub-graph of the graph for performingan odor data processing function.

In some implementations, the method further includes obtaining arepresentation of the sub-graph as a two-dimensional array of numericalvalues, where a value of a component of the array at position (i,j)indicates if the sub-graph includes an edge from node i to node j. Thearray is processed to identify a set of clusters in the array, whereeach cluster specifies a contiguous region of the array. Edges from thesub-graph that are not included in the identified clusters are removedprior to determining the artificial neural network architecturecorresponding to the sub-graph.

In some implementations, processing the array to identify the set ofclusters in the array includes processing the array using a blobdetection algorithm.

In some implementations, determining an artificial neural networkarchitecture corresponding to the sub-graph of the graph includesmapping each node in the sub-graph to a corresponding artificial neuronin the artificial neural network architecture. Each edge in thesub-graph is mapped to a connection between a pair of artificial neuronsin the artificial neural network architecture that correspond to thepair of nodes in the sub-graph that are connected by the edge.

In some implementations, obtaining data defining a graph representingsynaptic connectivity between neurons in a brain of a biologicalorganism includes: obtaining a synaptic resolution image of at least aportion of the brain of the biological organism; and processing theimage to identify: (i) a set of neurons in the brain, and (ii) a set ofsynaptic connections between pairs of neurons in the brain.

According to a fifth aspect, there is provided a system including one ormore computers and one or more storage devices storing instructions thatwhen executed by the one or more computers cause the one or morecomputers to implement a reservoir computing neural network. Thereservoir computing neural network is configured to receive a networkinput and to generate a network output from the network output, andcomprises: (i) a brain emulation sub-network, and (ii) a predictionsub-network. The brain emulation sub-network is configured to processthe network input in accordance with values of a set of brain emulationsub-network parameters to generate an alternative representation of thenetwork input. The prediction sub-network is configured to process thealternative representation of the network input in accordance withvalues of a set of prediction sub-network parameters to generate thenetwork output. The values of the brain emulation sub-network parametersare determined before the reservoir computing neural network is trainedand are not adjusting during training of the reservoir computing neuralnetwork. The values of the prediction sub-network parameters areadjusted during training of the reservoir computing neural network. Thebrain emulation sub-network has a neural network architecture that isspecified by a graph representing synaptic connectivity between neuronsin a brain of a biological organism. The graph includes a set of nodesand edges, where each edge connects a pair of nodes, each nodecorresponds to a respective neuron in the brain of the biologicalorganism, and each edge connecting a pair of nodes in the graphcorresponds to a synaptic connection between a pair of neurons in thebrain of the biological organism.

In some implementations, specifying the neural network architecture ofthe brain emulation sub-network by the graph representing synapticconnectivity between neurons in the brain of the biological organismincludes mapping each node in the graph to a corresponding artificialneuron in the neural network architecture of the brain emulationsub-network. Each edge in the graph is mapped to a connection between apair of artificial neurons in the neural network architecture of thebrain emulation sub-network that correspond to the pair of nodes in thegraph that are connected by the edge.

In some implementations, the graph representing synaptic connectivitybetween neurons in the brain of the biological organism is generated byprocessing a synaptic resolution image of at least a portion of thebrain of the biological organism to identify: (i) a set of neurons inthe brain, and (ii) a set of synaptic connections between pairs ofneurons in the brain.

In some implementations, the synaptic resolution image of the brain ofthe biological organism is generated using electron microscopytechniques.

In some implementations, the graph represents synaptic connectivitybetween neurons in the brain of the biological organism that arepredicted to have a particular function in the brain of the biologicalorganism.

In some implementations, the particular function is a visual dataprocessing function, an audio data processing function, or an odor dataprocessing function.

In some implementations, the values of the prediction sub-networkparameters are adjusted during training of the reservoir computingneural network to optimize an objective function.

In some implementations, the objective function includes a termcharacterizing a prediction accuracy of the reservoir computing neuralnetwork.

In some implementations, the term characterizing the prediction accuracyof the reservoir computing neural network includes a cross-entropy lossterm.

In some implementations, the objective function includes a termcharacterizing a magnitude of the values of the prediction sub-networkparameters.

In some implementations, dropout regularization is applied to the brainemulation sub-network parameters during training of the reservoircomputing neural network.

In some implementations, the reservoir computing neural network isconfigured to process a network input including image data, video data,audio data, odor data, point cloud data, magnetic field data, or acombination thereof.

In some implementations, the reservoir computing neural network isconfigured to generate a classification output that includes arespective score for each of a plurality of classes.

In some implementations, the neural network architecture of theprediction sub-network is less complex than the neural networkarchitecture of the brain emulation sub-network.

In some implementations, the prediction sub-network includes only asingle neural network layer.

In some implementations, the values of the brain emulation sub-networkparameters are determined based on weight values associated withsynaptic connections between neurons in the brain of the biologicalorganism.

According to a sixth aspect there is provided a method performed by oneor more data processing apparatus, the method including obtaining datadefining a synaptic connectivity graph representing synapticconnectivity between neurons in a brain of a biological organism. Thesynaptic connectivity graph includes a set of nodes and edges, whereeach edge connects a pair of nodes, each node corresponds to arespective neuron in the brain of the biological organism, and each edgeconnecting a pair of nodes in the synaptic connectivity graphcorresponds to a synaptic connection between a pair of neurons in thebrain of the biological organism. Data defining a set of candidategraphs is generated based on the synaptic connectivity graph. For eachcandidate graph, a performance measure on a machine learning task of aneural network having a neural network architecture that is specified bythe candidate graph is determined. A final neural network architectureis selected for performing the machine learning task based on theperformance measures.

In some implementations, obtaining data defining the synapticconnectivity graph representing synaptic connectivity between neurons inthe brain of the biological organism includes: obtaining a synapticresolution image of at least a portion of the brain of the biologicalorganism; and processing the image to identify: (i) a set of neurons inthe brain, and (ii) a set of synaptic connections between pairs ofneurons in the brain.

In some implementations, the synaptic resolution image of the brain ofthe biological organism is generated using electron microscopytechniques.

In some implementations, generating data defining the set of candidategraphs based on the synaptic connectivity graph includes, for each ofmultiple graph features: determining a value of the graph feature forthe synaptic connectivity graph; and determining a constraintcorresponding to the graph feature based on the value of the graphfeature for the synaptic connectivity graph. The constraintcorresponding to the graph feature specifies a target value or a rangeof target values of the graph feature for the candidate graphs. The setof candidate graphs are generated based on the constraints correspondingto the graph features.

In some implementations, the set of graph features includes one or moreof: (i) a graph feature that specifies a number of nodes in a graph,(ii) a graph feature that specifies a number of edges in a largestcluster in a two-dimensional array representing a graph, (iii) a graphfeature that specifies a number of clusters in a two-dimensional arrayrepresenting a graph that include a number of edges that is within apredefined range of values, (iv) an average path length between nodes ina graph, or (v) a maximum path length between nodes in a graph.

In some implementations, generating a candidate graph based on theconstraints corresponding to the graph features includes: initializingthe candidate graph; and at each of one or more iterations, updating thecandidate graph to cause the candidate graph to satisfy a correspondingconstraint.

In some implementations, initializing the candidate graph includesrandomly initializing the candidate graph.

In some implementations, generating data defining the set of candidategraphs based on the synaptic connectivity graph includes generating aset of current graphs based on the synaptic connectivity graph. Eachcurrent graph is generated by applying one or more random modificationsto the synaptic connectivity graph. The set of current graphs is updatedat each of multiple iterations, including, at each iteration: randomlysampling a plurality of current graphs from the set of current graphs;determining, for each sampled graph, a performance measure on themachine learning task of a neural network having a neural networkarchitecture that is specified by the sampled graph; and updating theset of current graphs based on the performance measures of the sampledgraphs. After a final iteration of the plurality of iterations, eachcurrent graph in the set of current graphs is identified as a candidategraph.

In some implementations, updating the set of current graphs based on theperformance measures of the sampled graphs includes removing any sampledgraph having a performance measure that does not satisfy a thresholdfrom the set of current graphs.

In some implementations, updating the set of current graphs based on theperformance measures of the sampled graphs includes: identifying one ormore of the sampled graphs having the highest performance measures;generating one or more new graphs based on the randomly sampled graphs,where each new graph is generated by applying one or more randommodifications to a sampled graph; and adding the new graphs to the setof current graphs.

In some implementations, determining a performance measure on a machinelearning task of a neural network having a neural network architecturethat is specified by a candidate graph includes determining the neuralnetwork architecture that is specified by the candidate graph, includingmapping each node in the candidate graph to a corresponding artificialneuron in the neural network architecture. Each edge in the candidategraph is mapped to a connection between a pair of artificial neurons inthe neural network architecture that correspond to the pair of nodes inthe candidate graph that are connected by the edge.

In some implementations, selecting a final neural network architecturefor performing the machine learning task based on the performancemeasures includes selecting the neural network architecture specified bythe candidate graph associated with the highest performance measure.

In some implementations, the machine learning task includes processingimage data to generate a classification of the image data.

In some implementations, the machine learning task includes processingaudio data to generate a classification of the audio data.

In some implementations, generating data defining the set of candidategraphs based on the synaptic connectivity graph includes, at each ofmultiple iterations, generating a candidate graph by applying one ormore transformation operations to the synaptic connectivity graph, wherethe transformation operations are specified by current values oftransformation operation parameters. A performance measure on themachine learning task of a neural network having a neural networkarchitecture that is specified by the candidate graph is determined. Thecurrent values of the transformation operation parameters are updatedbased at least in part on the performance measure.

According to a seventh aspect, there is provided a system including: oneor more computers; and one or more storage devices communicativelycoupled to the one or more computers, where the one or more storagedevices store instructions that, when executed by the one or morecomputers, cause the one or more computers to perform the operations ofthe method of any preceding aspect.

According to an eighth aspect, there are provided one or morenon-transitory computer storage media storing instructions that whenexecuted by one or more computers cause the one or more computers toperform the operations of the method of any preceding aspect.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages.

The systems described in this specification can implement a brainemulation neural network having an architecture specified by a synapticconnectivity graph derived from a synaptic resolution image of the brainof a biological organism. The brains of biological organisms may beadapted by evolutionary pressures to be effective at solving certaintasks, e.g., classifying objects or generating robust objectrepresentations, and brain emulation neural networks may share thiscapacity to effectively solve tasks. In particular, compared to otherneural networks, e.g., with manually specified neural networkarchitectures, brain emulation neural networks may require less trainingdata, fewer training iterations, or both, to effectively solve certaintasks. Moreover, brain emulation neural networks may perform certainmachine learning tasks more effectively, e.g., with higher accuracy,than other neural networks.

The systems described in this specification can process a synapticconnectivity graph corresponding to a brain to predict the neuronaltypes (e.g., primary sensory type, visual type, olfactory type, memorytype, and the like) of neurons in the brain. In particular, features canbe computed for each node in the graph (e.g., the path lengthcorresponding to the node and the number of edges connected to thenode), and the node features can be used to classify certain nodes ascorresponding to a type of neuron in the brain. A sub-graph of theoverall graph corresponding to neurons that are predicted to be of acertain type can be identified, and a brain emulation neural network maybe implemented with an architecture specified by the sub-graph, i.e.,rather than the entire graph. Implementing a brain emulation neuralnetwork with an architecture specified by a sub-graph corresponding toneurons of a certain type may enable the brain emulation neural networkto perform certain tasks more effectively while consuming fewercomputational resources (e.g. memory and computing power). In oneexample, the brain emulation neural network may be configured to performimage processing tasks, and the architecture of the brain emulationneural network may be specified by a sub-graph corresponding to only thevisual system of the brain (i.e., to visual type neurons).

The systems described in this specification can use a brain emulationneural network to train another neural network, referred to as a“student” neural network, having a substantially less complex neuralnetwork architecture. More specifically, the student neural network maybe trained to match outputs that are generated by the brain emulationneural network. The brains of many biological organisms have a largenumber of neurons, e.g., a fly brain may have on the order of ~10⁵neurons or more. Therefore, a brain emulation network may have a highlycomplex architecture, and processing data using a brain emulation neuralnetwork may be computationally expensive. After being trained, thestudent neural network may inherit the capacity of the brain emulationneural network to effectively solve certain tasks, while consuming fewercomputational resources than the brain emulation neural network due tohaving a substantially less complex neural network architecture.

The systems described in this specification can use a brain emulationneural network in reservoir computing applications. In particular, a“reservoir computing” neural network may be implemented with anarchitecture specified by a brain emulation sub-network followed by a“prediction” sub-network. Generally, the prediction sub-network may havea substantially less complex architecture than the brain emulationneural network, e.g., the prediction sub-network may consist of a singleclassification layer. During training of the reservoir computing neuralnetwork, only the weights of the prediction sub-network are trained,while the weights of the brain emulation neural network are consideredstatic and are not trained. Generally, a brain emulation neural networkmay have a very large number of trainable parameters and a highlyrecurrent architecture. Therefore training the brain emulation neuralnetwork may be computationally-intensive and prone to failure, e.g., asa result of the model parameter values of the brain emulation neuralnetwork oscillating rather than converging to fixed values. Thereservoir computing neural network described in this specification mayharness the capacity of the brain emulation neural network, e.g., togenerate representations that are effective for solving tasks, withoutrequiring the brain emulation neural network to be trained.

This specification further describes techniques for using the synapticconnectivity graph to “seed” (i.e., initialize) a search through a spaceof possible neural network architectures to identify an architecturethat can be used to effectively perform a machine learning task. Morespecifically, the synaptic connectivity graph may be used to derive“candidate” graphs which specify corresponding neural networkarchitectures, and the best performing of these architectures may beselected to perform the machine learning task. Seeding the neuralarchitecture search process using the synaptic connectivity graph mayfacilitate the discovery of large numbers of biologically-inspiredneural network architectures, some of which may be effective forperforming machine learning tasks.

The details of one or more embodiments of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of generating a brain emulation neuralnetwork based on a synaptic resolution image of the brain of abiological organism.

FIG. 2 shows an example data flow for generating a synaptic connectivitygraph and a brain emulation neural network based on the brain of abiological organism.

FIG. 3 shows an example architecture mapping system.

FIG. 4 illustrates an example graph and an example sub-graph.

FIG. 5 shows an example adversarial training system.

FIG. 6 shows an example distillation training system.

FIG. 7 shows an example reservoir computing system.

FIG. 8A shows an example architecture search system.

FIG. 8B shows an example constraint satisfaction system.

FIG. 8C shows an example evolutionary system.

FIG. 8D shows an example optimization system.

FIG. 9 is a flow diagram of an example process for generating a brainemulation neural network.

FIG. 10 is a flow diagram of an example process for determining anartificial neural network architecture corresponding to a sub-graph of asynaptic connectivity graph.

FIG. 11 is a flow diagram of an example process for adversarial trainingof a student neural network using a brain emulation neural network.

FIG. 12 is a flow diagram of an example process for distillationtraining of a student neural network using a brain emulation neuralnetwork.

FIG. 13 is a flow diagram of an example process for processing datausing a reservoir computing neural network that includes: (i) a brainemulation sub-network, and (ii) a prediction sub-network.

FIG. 14 is a flow diagram of an example process for seeding a neuralarchitecture search procedure using a synaptic connectivity graph.

FIG. 15 is a block diagram of an example computer system.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 illustrates an example of generating an artificial (i.e.,computer implemented) brain emulation neural network 100 based on asynaptic resolution image 102 of the brain 104 of a biological organism106, e.g., a fly. The synaptic resolution image 102 may be processed togenerate a synaptic connectivity graph 108, e.g., where each node of thegraph 108 corresponds to a neuron in the brain 104, and two nodes in thegraph 108 are connected if the corresponding neurons in the brain 104share a synaptic connection. The structure of the graph 108 may be usedto specify the architecture of the brain emulation neural network 100.For example, each node of the graph 108 may mapped to an artificialneuron, a neural network layer, or a group of neural network layers inthe brain emulation neural network 100. Further, each edge of the graph108 may be mapped to a connection between artificial neurons, layers, orgroups of layers in the brain emulation neural network 100. The brain104 of the biological organism 106 may be adapted by evolutionarypressures to be effective at solving certain tasks, e.g., classifyingobjects or generating robust object representations, and the brainemulation neural network 100 may share this capacity to effectivelysolve tasks. These features and other features are described in moredetail below.

FIG. 2 shows an example data flow 200 for generating a synapticconnectivity graph 202 and a brain emulation neural network 204 based onthe brain 206 of a biological organism. As used throughout thisdocument, a brain may refer to any amount of nervous tissue from anervous system of a biological organism, and nervous tissue may refer toany tissue that includes neurons (i.e., nerve cells). The biologicalorganism may be, e.g., a worm, a fly, a mouse, a cat, or a human.

An imaging system 208 may be used to generate a synaptic resolutionimage 210 of the brain 206. An image of the brain 206 may be referred toas having synaptic resolution if it has a spatial resolution that issufficiently high to enable the identification of at least some synapsesin the brain 206. Put another way, an image of the brain 206 may bereferred to as having synaptic resolution if it depicts the brain 206 ata magnification level that is sufficiently high to enable theidentification of at least some synapses in the brain 206. The image 210may be a volumetric image, i.e., that characterizes a three-dimensionalrepresentation of the brain 206. The image 210 may be represented in anyappropriate format, e.g., as a three-dimensional array of numericalvalues.

The imaging system 208 may be any appropriate system capable ofgenerating synaptic resolution images, e.g., an electron microscopysystem. The imaging system 208 may process “thin sections” from thebrain 206 (i.e., thin slices of the brain attached to slides) togenerate output images that each have a field of view corresponding to aproper subset of a thin section. The imaging system 208 may generate acomplete image of each thin section by stitching together the imagescorresponding to different fields of view of the thin section using anyappropriate image stitching technique. The imaging system 208 maygenerate the volumetric image 210 of the brain by registering andstacking the images of each thin section. Registering two images refersto applying transformation operations (e.g., translation or rotationoperations) to one or both of the images to align them. Exampletechniques for generating a synaptic resolution image of a brain aredescribed with reference to: Z. Zheng, et al., “A complete electronmicroscopy volume of the brain of adult Drosophila melanogaster,” Cell174, 730-743 (2018).

A graphing system 212 is configured to process the synaptic resolutionimage 210 to generate the synaptic connectivity graph 202. The synapticconnectivity graph 202 specifies a set of nodes and a set of edges, suchthat each edge connects two nodes. To generate the graph 202, thegraphing system 212 identifies each neuron in the image 210 as arespective node in the graph, and identifies each synaptic connectionbetween a pair of neurons in the image 210 as an edge between thecorresponding pair of nodes in the graph.

The graphing system 212 may identify the neurons and the synapsesdepicted in the image 210 using any of a variety of techniques. Forexample, the graphing system 212 may process the image 210 to identifythe positions of the neurons depicted in the image 210, and determinewhether a synapse connects two neurons based on the proximity of theneurons (as will be described in more detail below). In this example,the graphing system 212 may process an input including: (i) the image,(ii) features derived from the image, or (iii) both, using a machinelearning model that is trained using supervised learning techniques toidentify neurons in images. The machine learning model may be, e.g., aconvolutional neural network model or a random forest model. The outputof the machine learning model may include a neuron probability map thatspecifies a respective probability that each voxel in the image isincluded in a neuron. The graphing system 212 may identify contiguousclusters of voxels in the neuron probability map as being neurons.

Optionally, prior to identifying the neurons from the neuron probabilitymap, the graphing system 212 may apply one or more filtering operationsto the neuron probability map, e.g., with a Gaussian filtering kernel.Filtering the neuron probability map may reduce the amount of “noise” inthe neuron probability map, e.g., where only a single voxel in a regionis associated with a high likelihood of being a neuron.

The machine learning model used by the graphing system 212 to generatethe neuron probability map may be trained using supervised learningtraining techniques on a set of training data. The training data mayinclude a set of training examples, where each training examplespecifies: (i) a training input that can be processed by the machinelearning model, and (ii) a target output that should be generated by themachine learning model by processing the training input. For example,the training input may be a synaptic resolution image of a brain, andthe target output may be a “label map” that specifies a label for eachvoxel of the image indicating whether the voxel is included in a neuron.The target outputs of the training examples may be generated by manualannotation, e.g., where a person manually specifies which voxels of atraining input are included in neurons.

Example techniques for identifying the positions of neurons depicted inthe image 210 using neural networks (in particular, flood-filling neuralnetworks) are described with reference to: P.H. Li et al.: “AutomatedReconstruction of a Serial-Section EM Drosophila Brain withFlood-Filling Networks and Local Realignment,” bioRxiv doi:10.1101/605634 (2019).

The graphing system 212 may identify the synapses connecting the neuronsin the image 210 based on the proximity of the neurons. For example, thegraphing system 212 may determine that a first neuron is connected by asynapse to a second neuron based on the area of overlap between: (i) atolerance region in the image around the first neuron, and (ii) atolerance region in the image around the second neuron. That is, thegraphing system 212 may determine whether the first neuron and thesecond neuron are connected based on the number of spatial locations(e.g., voxels) that are included in both: (i) the tolerance regionaround the first neuron, and (ii) the tolerance region around the secondneuron. For example, the graphing system 212 may determine that twoneurons are connected if the overlap between the tolerance regionsaround the respective neurons includes at least a predefined number ofspatial locations (e.g., one spatial location). A “tolerance region”around a neuron refers to a contiguous region of the image that includesthe neuron. For example, the tolerance region around a neuron may bespecified as the set of spatial locations in the image that are either:(i) in the interior of the neuron, or (ii) within a predefined distanceof the interior of the neuron.

The graphing system 212 may further identify a weight value associatedwith each edge in the graph 202. For example, the graphing system 212may identify a weight for an edge connecting two nodes in the graph 202based on the area of overlap between the tolerance regions around therespective neurons corresponding to the nodes in the image 210. The areaof overlap may be measured, e.g., as the number of voxels in the image210 that are contained in the overlap of the respective toleranceregions around the neurons. The weight for an edge connecting two nodesin the graph 202 may be understood as characterizing the (approximate)strength of the connection between the corresponding neurons in thebrain (e.g., the amount of information flow through the synapseconnecting the two neurons).

In addition to identifying synapses in the image 210, the graphingsystem 212 may further determine the direction of each synapse using anyappropriate technique. The “direction” of a synapse between two neuronsrefers to the direction of information flow between the two neurons,e.g., if a first neuron uses a synapse to transmit signals to a secondneuron, then the direction of the synapse would point from the firstneuron to the second neuron. Example techniques for determining thedirections of synapses connecting pairs of neurons are described withreference to: C. Seguin, A. Razi, and A. Zalesky: “Inferring neuralsignalling directionality from undirected structure connectomes,” NatureCommunications 10, 4289 (2019), doi:10.1038/s41467-019-12201-w.

In implementations where the graphing system 212 determines thedirections of the synapses in the image 210, the graphing system 212 mayassociate each edge in the graph 202 with direction of the correspondingsynapse. That is, the graph 202 may be a directed graph. In otherimplementations, the graph 202 may be an undirected graph, i.e., wherethe edges in the graph are not associated with a direction.

The graph 202 may be represented in any of a variety of ways. Forexample, the graph 202 may be represented as a two-dimensional array ofnumerical values with a number of rows and columns equal to the numberof nodes in the graph. The component of the array at position (i,j) mayhave value 1 if the graph includes an edge pointing from node i to nodej, and value 0 otherwise. In implementations where the graphing system212 determines a weight value for each edge in the graph 202, the weightvalues may be similarly represented as a two-dimensional array ofnumerical values. More specifically, if the graph includes an edgeconnecting node i to node j, the component of the array at position(i,j) may have a value given by the corresponding edge weight, andotherwise the component of the array at position (i,j) may have value 0.

An architecture mapping system 300 may process the synaptic connectivitygraph 202 to determine the architecture of the brain emulation neuralnetwork 204. For example, the architecture mapping system 300 may mapeach node in the graph 202 to: (i) an artificial neuron, (ii) a neuralnetwork layer, or (iii) a group of neural network layers, in thearchitecture of the brain emulation neural network 204. The architecturemapping system 300 may further map each edge of the graph 202 to aconnection in the brain emulation neural network 204, e.g., such that afirst artificial neuron that is connected to a second artificial neuronis configured to provide its output to the second artificial neuron. Insome implementations, the architecture mapping system 300 may apply oneor more transformation operations to the graph 202 before mapping thenodes and edges of the graph 202 to corresponding components in thearchitecture of the brain emulation neural network 204, as will bedescribed in more detail below. An example architecture mapping system300 is described in more detail with reference to FIG. 3 .

The brain emulation neural network 204 may be provided to one or moreof: the direct training system 214, the adversarial training system 500,the distillation training system 600, or the reservoir computing system700, each of which will be described in more detail next.

The direct training system 214 is configured to train the brainemulation neural network 204 using machine learning training techniqueson a set of training data. The training data may include multipletraining examples, where each training example specifies: (i) a traininginput, and (ii) a corresponding target output that should be generatedby the brain emulation neural network 204 by processing the traininginput. In one example, the direct training system 214 may train thebrain emulation neural network 204 over multiple training iterationsusing a stochastic gradient descent optimization technique. In thisexample, at each training iteration, the direct training system 214 maysample a “batch” (set) of one or more training examples from thetraining data, and process the training inputs specified by the trainingexamples to generate corresponding network outputs. The direct trainingsystem 214 may evaluate an objective function that measures a similaritybetween: (i) the target outputs specified by the training examples, and(ii) the network outputs generated by the brain emulation neuralnetwork, e.g., a cross-entropy or squared-error objective function. Thedirect training system 214 may determine gradients of the objectivefunction, e.g., using backpropagation techniques, and update theparameter values of the brain emulation neural network 204 using thegradients, e.g., using any appropriate gradient descent optimizationalgorithm, e.g., RMSprop or Adam.

The adversarial training system 500 and the distillation training system600 are configured to use the brain emulation neural network 204 tofacilitate training of a “student” neural network having a less complexarchitecture than the brain emulation neural network 204. The complexityof a neural network architecture may be measured, e.g., by the number ofparameters required to specify the operations performed by the neuralnetwork. The adversarial training system 500 and the distillationtraining system 600 may train the student neural network to match theoutputs generated by the brain emulation neural network. After training,the student neural network may inherit the capacity of the brainemulation neural network to effectively solve certain tasks, whileconsuming fewer computational resources (e.g., memory and computingpower) than the brain emulation neural network. An example adversarialtraining system 500 is described with reference to FIG. 5 , and anexample distillation training system 600 is described with reference toFIG. 6 .

The reservoir computing system 700 uses the brain emulation neuralnetwork 204 as a sub-network of a “reservoir computing” neural network.The reservoir computing neural network is configured to process anetwork input using the brain emulation neural network 204 to generatean alternative representation of the network input, and process thealternative representation of the network input using a “prediction”sub-network to generate a network output. During training of thereservoir computing neural network, the parameter values of theprediction sub-network are trained, but the parameter values of thebrain emulation neural network 204 are static, i.e., are not trained.Instead of being trained, the parameter values of the brain emulationneural network 204 may be determined from the weight values of the edgesof the synaptic connectivity graph, as will be described in more detailbelow. The reservoir computing system 700 facilitates application of thebrain emulation neural network to machine learning tasks by obviatingthe need to train the parameter values of the brain emulation neuralnetwork 204. An example reservoir computing system 700 is described inmore detail with reference to FIG. 7 .

The architecture search system 800 uses the synaptic connectivity graph202 characterizing the biological brain 206 to seed (i.e., initialize) asearch procedure to identify a neural network architecture 216 that iseffective for solving a machine learning task. More specifically, thearchitecture search system 800 uses the synaptic connectivity graph 202to generate a set of “candidate” graphs and determines an effectivenessof the neural network architecture specified by each candidate graph forperforming the machine learning task. The architecture search system 800may identify the candidate graph specifying the most effective neuralnetwork architecture, and thereafter provide a neural network havingthis task-specific neural network architecture 216 for performing themachine learning task. An example architecture search system 800 isdescribed in more detail with reference to FIGS. 8 .

FIG. 3 shows an example architecture mapping system 300. Thearchitecture mapping system 300 is an example of a system implemented ascomputer programs on one or more computers in one or more locations inwhich the systems, components, and techniques described below areimplemented.

The architecture mapping system 300 is configured to process a synapticconnectivity graph 202 to determine a corresponding neural networkarchitecture 302 of a brain emulation neural network 204. Thearchitecture mapping system 300 may determine the architecture 302 usingone or more of: a transformation engine 304, a feature generation engine306, a node classification engine 308, and a nucleus classificationengine 310, which will each be described in more detail next.

The transformation engine 304 may be configured to apply one or moretransformation operations to the synaptic connectivity graph 202 thatalter the connectivity of the graph 202, i.e., by adding or removingedges from the graph. A few examples of transformation operationsfollow.

In one example, to apply a transformation operation to the graph 202,the transformation engine 304 may randomly sample a set of node pairsfrom the graph (i.e., where each node pair specifies a first node and asecond node). For example, the transformation engine may sample apredefined number of node pairs in accordance with a uniform probabilitydistribution over the set of possible node pairs. For each sampled nodepair, the transformation engine 304 may modify the connectivity betweenthe two nodes in the node pair with a predefined probability (e.g.,0.1%). In one example, the transformation engine 304 may connect thenodes by an edge (i.e., if they are not already connected by an edge)with the predefined probability. In another example, the transformationengine 304 may reverse the direction of any edge connecting the twonodes with the predefined probability. In another example, thetransformation engine 304 may invert the connectivity between the twonodes with the predefined probability, i.e., by adding an edge betweenthe nodes if they are not already connected, and by removing the edgebetween the nodes if they are already connected.

In another example, the transformation engine 304 may apply aconvolutional filter to a representation of the graph 202 as atwo-dimensional array of numerical values. As described above, the graph202 may be represented as a two-dimensional array of numerical valueswhere the component of the array at position (i,j) may have value 1 ifthe graph includes an edge pointing from node i to node j, and value 0otherwise. The convolutional filter may have any appropriate kernel,e.g., a spherical kernel or a Gaussian kernel. After applying theconvolutional filter, the transformation engine 304 may quantize thevalues in the array representing the graph, e.g., by rounding each valuein the array to 0 or 1, to cause the array to unambiguously specify theconnectivity of the graph. Applying a convolutional filter to therepresentation of the graph 202 may have the effect of regularizing thegraph, e.g., by smoothing the values in the array representing the graphto reduce the likelihood of a component in the array having a differentvalue than many its neighbors.

In some cases, the graph 202 may include some inaccuracies inrepresenting the synaptic connectivity in the biological brain. Forexample, the graph may include nodes that are not connected by an edgedespite the corresponding neurons in the brain being connected by asynapse, or “spurious” edges that connect nodes in the graph despite thecorresponding neurons in the brain not being connected by a synapse.Inaccuracies in the graph may result, e.g., from imaging artifacts orambiguities in the synaptic resolution image of the brain that isprocessed to generate the graph. Regularizing the graph, e.g., byapplying a convolutional filter to the representation of the graph, mayincrease the accuracy with which the graph represents the synapticconnectivity in the brain, e.g., by removing spurious edges.

The architecture mapping system 300 may use the feature generationengine 306 and the node classification engine 308 to determine predicted“types” 310 of the neurons corresponding to the nodes in the graph 202.The type of a neuron may characterize any appropriate aspect of theneuron. In one example, the type of a neuron may characterize thefunction performed by the neuron in the brain, e.g., a visual functionby processing visual data, an olfactory function by processing odordata, or a memory function by retaining information. After identifyingthe types of the neurons corresponding to the nodes in the graph 202,the architecture mapping system 300 may identify a sub-graph 312 of theoverall graph 202 based on the neuron types, and determine the neuralnetwork architecture 302 based on the sub-graph 312. The featuregeneration engine 306 and the node classification engine 308 aredescribed in more detail next.

The feature generation engine 306 may be configured to process the graph202 (potentially after it has been modified by the transformation engine304) to generate one or more respective node features 314 correspondingto each node of the graph 202. The node features corresponding to a nodemay characterize the topology (i.e., connectivity) of the graph relativeto the node. In one example, the feature generation engine 306 maygenerate a node degree feature for each node in the graph 202, where thenode degree feature for a given node specifies the number of other nodesthat are connected to the given node by an edge. In another example, thefeature generation engine 306 may generate a path length feature foreach node in the graph 202, where the path length feature for a nodespecifies the length of the longest path in the graph starting from thenode. A path in the graph may refer to a sequence of nodes in the graph,such that each node in the path is connected by an edge to the next nodein the path. The length of a path in the graph may refer to the numberof nodes in the path. In another example, the feature generation engine306 may generate a neighborhood size feature for each node in the graph202, where the neighborhood size feature for a given node specifies thenumber of other nodes that are connected to the node by a path of lengthat most N. In this example, N may be a positive integer value. Inanother example, the feature generation engine 306 may generate aninformation flow feature for each node in the graph 202. The informationflow feature for a given node may specify the fraction of the edgesconnected to the given node that are outgoing edges, i.e., the fractionof edges connected to the given node that point from the given node to adifferent node.

In some implementations, the feature generation engine 306 may generateone or more node features that do not directly characterize the topologyof the graph relative to the nodes. In one example, the featuregeneration engine 306 may generate a spatial position feature for eachnode in the graph 202, where the spatial position feature for a givennode specifies the spatial position in the brain of the neuroncorresponding to the node, e.g., in a Cartesian coordinate system of thesynaptic resolution image of the brain. In another example, the featuregeneration engine 306 may generate a feature for each node in the graph202 indicating whether the corresponding neuron is excitatory orinhibitory. In another example, the feature generation engine 306 maygenerate a feature for each node in the graph 202 that identifies theneuropil region associated with the neuron corresponding to the node.

In some cases, the feature generation engine 306 may use weightsassociated with the edges in the graph in determining the node features314. As described above, a weight value for an edge connecting two nodesmay be determined, e.g., based on the area of any overlap betweentolerance regions around the neurons corresponding to the nodes. In oneexample, the feature generation engine 306 may determine the node degreefeature for a given node as a sum of the weights corresponding to theedges that connect the given node to other nodes in the graph. Inanother example, the feature generation engine 306 may determine thepath length feature for a given node as a sum of the edge weights alongthe longest path in the graph starting from the node.

The node classification engine 308 may be configured to process the nodefeatures 314 to identify a predicted neuron type 310 corresponding tocertain nodes of the graph 202. In one example, the node classificationengine 308 may process the node features 314 to identify a proper subsetof the nodes in the graph 202 with the highest values of the path lengthfeature. For example, the node classification engine 308 may identifythe nodes with a path length feature value greater than the 90thpercentile (or any other appropriate percentile) of the path lengthfeature values of all the nodes in the graph. The node classificationengine 308 may then associate the identified nodes having the highestvalues of the path length feature with the predicted neuron type of“primary sensory neuron.” In another example, the node classificationengine 308 may process the node features 314 to identify a proper subsetof the nodes in the graph 202 with the highest values of the informationflow feature, i.e., indicating that many of the edges connected to thenode are outgoing edges. The node classification engine 308 may thenassociate the identified nodes having the highest values of theinformation flow feature with the predicted neuron type of “sensoryneuron.” In another example, the node classification engine 308 mayprocess the node features 314 to identify a proper subset of the nodesin the graph 202 with the lowest values of the information flow feature,i.e., indicating that many of the edges connected to the node areincoming edges (i.e., edges that point towards the node). The nodeclassification engine 308 may then associate the identified nodes havingthe lowest values of the information flow feature with the predictedneuron type of “associative neuron.”

The architecture mapping system 300 may identify a sub-graph 312 of theoverall graph 202 based on the predicted neuron types 310 correspondingto the nodes of the graph 202. A “sub-graph” may refer to a graphspecified by: (i) a proper subset of the nodes of the graph 202, and(ii) a proper subset of the edges of the graph 202. FIG. 4 provides anillustration of an example sub-graph of an overall graph. In oneexample, the architecture mapping system 300 may select: (i) each nodein the graph 202 corresponding to particular neuron type, and (ii) eachedge in the graph 202 that connects nodes in the graph corresponding tothe particular neuron type, for inclusion in the sub-graph 312. Theneuron type selected for inclusion in the sub-graph may be, e.g., visualneurons, olfactory neurons, memory neurons, or any other appropriatetype of neuron. In some cases, the architecture mapping system 300 mayselect multiple neuron types for inclusion in the sub-graph 312, e.g.,both visual neurons and olfactory neurons.

The type of neuron selected for inclusion in the sub-graph 312 may bedetermined based on the task which the brain emulation neural network204 will be configured to perform. In one example, the brain emulationneural network 204 may be configured to perform an image processingtask, and neurons that are predicted to perform visual functions (i.e.,by processing visual data) may be selected for inclusion in thesub-graph 312. In another example, the brain emulation neural network204 may be configured to perform an odor processing task, and neuronsthat are predicted to perform odor processing functions (i.e., byprocessing odor data) may be selected for inclusion in the sub-graph312. In another example, the brain emulation neural network 204 may beconfigured to perform an audio processing task, and neurons that arepredicted to perform audio processing (i.e., by processing audio data)may be selected for inclusion in the sub-graph 312.

If the edges of the graph 202 are associated with weight values (asdescribed above), then each edge of the sub-graph 312 may be associatedwith the weight value of the corresponding edge in the graph 202. Thesub-graph 312 may be represented, e.g., as a two-dimensional array ofnumerical values, as described with reference to the graph 202.

Determining the architecture 302 of the brain emulation neural network204 based on the sub-graph 312 rather than the overall graph 202 mayresult in the architecture 302 having a reduced complexity, e.g.,because the sub-graph 312 has fewer nodes, fewer edges, or both than thegraph 202. Reducing the complexity of the architecture 302 may reduceconsumption of computational resources (e.g., memory and computingpower) by the brain emulation neural network 204, e.g., enabling thebrain emulation neural network 204 to be deployed inresource-constrained environments, e.g., mobile devices. Reducing thecomplexity of the architecture 302 may also facilitate training of thebrain emulation neural network 204, e.g., by reducing the amount oftraining data required to train the brain emulation neural network 204to achieve an threshold level of performance (e.g., predictionaccuracy).

In some cases, the architecture mapping system 300 may further reducethe complexity of the architecture 302 using a nucleus classificationengine 310. In particular, the architecture mapping system 300 mayprocess the sub-graph 312 using the nucleus classification engine 310prior to determining the architecture 302. The nucleus classificationengine 310 may be configured to process a representation of thesub-graph 312 as a two-dimensional array of numerical values (asdescribed above) to identify one or more “clusters” in the array.

A cluster in the array representing the sub-graph 312 may refer to acontiguous region of the array such that at least a threshold fractionof the components in the region have a value indicating that an edgeexists between the pair of nodes corresponding to the component. In oneexample, the component of the array in position (i,j) may have value 1if an edge exists from node i to node j, and value 0 otherwise. In thisexample, the nucleus classification engine 310 may identify contiguousregions of the array such that at least a threshold fraction of thecomponents in the region have the value 1. The nucleus classificationengine 310 may identify clusters in the array representing the sub-graph312 by processing the array using a blob detection algorithm, e.g., byconvolving the array with a Gaussian kernel and then applying theLaplacian operator to the array. After applying the Laplacian operator,the nucleus classification engine 310 may identify each component of thearray having a value that satisfies a predefined threshold as beingincluded in a cluster.

Each of the clusters identified in the array representing the sub-graph312 may correspond to edges connecting a “nucleus” (i.e., group) ofrelated neurons in brain, e.g., a thalamic nucleus, a vestibularnucleus, a dentate nucleus, or a fastigial nucleus. After the nucleusclassification engine 310 identifies the clusters in the arrayrepresenting the sub-graph 312, the architecture mapping system 300 mayselect one or more of the clusters for inclusion in the sub-graph 312.The architecture mapping system 300 may select the clusters forinclusion in the sub-graph 312 based on respective features associatedwith each of the clusters. The features associated with a cluster mayinclude, e.g., the number of edges (i.e., components of the array) inthe cluster, the average of the node features corresponding to each nodethat is connected by an edge in the cluster, or both. In one example,the architecture mapping system 300 may select a predefined number oflargest clusters (i.e., that include the greatest number of edges) forinclusion in the sub-graph 312.

The architecture mapping system 300 may reduce the sub-graph 312 byremoving any edge in the sub-graph 312 that is not included in one ofthe selected clusters, and then map the reduced sub-graph 312 to acorresponding neural network architecture, as will be described in moredetail below. Reducing the sub-graph 312 by restricting it to includeonly edges that are included in selected clusters may further reduce thecomplexity of the architecture 302, thereby reducing computationalresource consumption by the brain emulation neural network 204 andfacilitating training of the brain emulation neural network 204.

The architecture mapping system 300 may determine the architecture 302of the brain emulation neural network 204 from the sub-graph 312 in anyof a variety of ways. For example, the architecture mapping system 300may map each node in the sub-graph 312 to a corresponding: (i)artificial neuron, (ii) artificial neural network layer, or (iii) groupof artificial neural network layers in the architecture 302, as will bedescribed in more detail next.

In one example, the neural network architecture 302 may include: (i) arespective artificial neuron corresponding to each node in the sub-graph312, and (ii) a respective connection corresponding to each edge in thesub-graph 312. In this example, the sub-graph 312 may be a directedgraph, and an edge that points from a first node to a second node in thesub-graph 312 may specify a connection pointing from a correspondingfirst artificial neuron to a corresponding second artificial neuron inthe architecture 302. The connection pointing from the first artificialneuron to the second artificial neuron may indicate that the output ofthe first artificial neuron should be provided as an input to the secondartificial neuron. Each connection in the architecture may be associatedwith a weight value, e.g., that is specified by the weight valueassociated with the corresponding edge in the sub-graph. An artificialneuron may refer to a component of the architecture 302 that isconfigured to receive one or more inputs (e.g., from one or more otherartificial neurons), and to process the inputs to generate an output.The inputs to an artificial neuron and the output generated by theartificial neuron may be represented as scalar numerical values. In oneexample, a given artificial neuron may generate an output b as:

$b = \sigma( {\sum\limits_{i = 1}^{n}{w_{i} \cdot a_{i}}} )$

where σ(•) is a non-linear “activation” function (e.g., a sigmoidfunction or an arctangent function),

{a_(i)}_(i = 1)^(n)

are the inputs provided to the given artificial neuron, and

{w_(i)}_(i = 1)^(n)

are the weight values associated with the connections between the givenartificial neuron and each of the other artificial neurons that providean input to the given artificial neuron.

In another example, the sub-graph 312 may be an undirected graph, andthe architecture mapping system 300 may map an edge that connects afirst node to a second node in the sub-graph 312 to two connectionsbetween a corresponding first artificial neuron and a correspondingsecond artificial neuron in the architecture. In particular, thearchitecture mapping system 300 may map the edge to: (i) a firstconnection pointing from the first artificial neuron to the secondartificial neuron, and (ii) a second connection pointing from the secondartificial neuron to the first artificial neuron.

In another example, the sub-graph 312 may be an undirected graph, andthe architecture mapping system may map an edge that connects a firstnode to a second node in the sub-graph 312 to one connection between acorresponding first artificial neuron and a corresponding secondartificial neuron in the architecture. The architecture mapping system300 may determine the direction of the connection between the firstartificial neuron and the second artificial neuron, e.g., by randomlysampling the direction in accordance with a probability distributionover the set of two possible directions.

In some cases, the edges in the sub-graph 312 may not be associated withweight values, and the weight values corresponding to the connections inthe architecture 302 may be determined randomly. For example, the weightvalue corresponding to each connection in the architecture 302 may berandomly sampled from a predetermined probability distribution, e.g., astandard Normal (N(0,1)) probability distribution.

In another example, the neural network architecture 302 may include: (i)a respective artificial neural network layer corresponding to each nodein the sub-graph 312, and (ii) a respective connection corresponding toeach edge in the sub-graph 312. In this example, a connection pointingfrom a first layer to a second layer may indicate that the output of thefirst layer should be provided as an input to the second layer. Anartificial neural network layer may refer to a collection of artificialneurons, and the inputs to a layer and the output generated by the layermay be represented as ordered collections of numerical values (e.g.,tensors of numerical values). In one example, the architecture 302 mayinclude a respective convolutional neural network layer corresponding toeach node in the sub-graph 312, and each given convolutional layer maygenerate an output d as:

$d = \sigma( {h_{\theta}( {\sum\limits_{i = 1}^{n}{w_{i} \cdot c_{i}}} )} )$

where each c_(i) (i = 1, ..., n) is a tensor (e.g., a two- or three-dimensional array) of numerical values provided as an input to thelayer, each w_(i) (i = 1, ..., n) is a weight value associated with theconnection between the given layer and each of the other layers thatprovide an input to the given layer (where the weight value for eachedge may be specified by the weight value associated with thecorresponding edge in the sub-graph), h_(θ)(•) represents the operationof applying one or more convolutional kernels to an input to generate acorresponding output, and σ(•) is a non-linear activation function thatis applied element-wise to each component of its input. In this example,each convolutional kernel may be represented as an array of numericalvalues, e.g., where each component of the array is randomly sampled froma predetermined probability distribution, e.g., a standard Normalprobability distribution.

In another example, the architecture mapping system 300 may determinethat the neural network architecture includes: (i) a respective group ofartificial neural network layers corresponding to each node in thesub-graph 312, and (ii) a respective connection corresponding to eachedge in the sub-graph 312. The layers in a group of artificial neuralnetwork layers corresponding to a node in the sub-graph 312 may beconnected, e.g., as a linear sequence of layers, or in any otherappropriate manner.

The neural network architecture 302 may include one or more artificialneurons that are identified as “input” artificial neurons and one ormore artificial neurons that are identified as “output” artificialneurons. An input artificial neuron may refer to an artificial neuronthat is configured to receive an input from a source that is external tothe brain emulation neural network 204. An output artificial neuralneuron may refer to an artificial neuron that generates an output whichis considered part of the overall output generated by the brainemulation neural network 204. The architecture mapping system 300 mayadd artificial neurons to the architecture 302 in addition to thosespecified by nodes in the sub-graph 312 (or the graph 202), anddesignate the added neurons as input artificial neurons and outputartificial neurons. For example, for a brain emulation neural network204 that is configured to process an input including a 100 × 100 imageto generate an output indicating whether the image is included in eachof 1000 categories, the architecture mapping system 300 may add 10,000(= 100 × 100) input artificial neurons and 1000 output artificialneurons to the architecture. Input and output artificial neurons thatare added to the architecture 302 may be connected to the other neuronsin the architecture in any of a variety of ways. For example, the inputand output artificial neurons may be densely connected to every otherneuron in the architecture.

Various operations performed by the described architecture mappingsystem 300 are optional or may be implemented in a different order. Forexample, the architecture mapping system 300 may refrain from applyingtransformation operations to the graph 202 using the transformationengine 304, and refrain from extracting a sub-graph 312 from the graph202 using the feature generation engine 306, the node classificationengine 308, and the nucleus classification engine 310. In this example,the architecture mapping system 300 may directly map the graph 202 tothe neural network architecture 302, e.g., by mapping each node in thegraph to an artificial neuron and mapping each edge in the graph to aconnection in the architecture, as described above.

FIG. 4 illustrates an example graph 400 and an example sub-graph 402.Each node in the graph 400 is represented by a circle (e.g., 404 and406), and each edge in the graph 400 is represented by a line (e.g., 408and 410). In this illustration, the graph 400 may be considered asimplified representation of a synaptic connectivity graph (an actualsynaptic connectivity graph may have far more nodes and edges than aredepicted in FIG. 4 ). A sub-graph 402 may be identified in the graph400, where the sub-graph 402 includes a proper subset of the nodes andedges of the graph 400. In this example, the nodes included in thesub-graph 402 are hatched (e.g., 406) and the edges included insub-graph 402 are dashed (e.g., 410). The nodes included in thesub-graph 402 may correspond to neurons of a particular type, e.g.,neurons having a particular function, e.g., olfactory neurons, visualneurons, or memory neurons. The architecture of the brain emulationneural network may be specified by the structure of the entire graph400, or by the structure of a sub-graph 402, as described above.

FIG. 5 shows an example adversarial training system 500. The adversarialtraining system 500 is an example of a system implemented as computerprograms on one or more computers in one or more locations in which thesystems, components, and techniques described below are implemented.

The adversarial training system 500 is configured to train a “student”neural network 502 to generate outputs that imitate (i.e., that havesimilar characteristics to) outputs generated by a brain emulationneural network 204. More specifically, the adversarial training system500 jointly trains the student neural network 502 and a “discriminator”neural network 504. The adversarial training system 500 trains thediscriminator neural network 504 to process an input to generate adiscriminative score that classifies whether the input was generated by:(i) the student neural network 502, or (ii) the brain emulation neuralnetwork 204. In parallel, the adversarial training system 500 trains thestudent neural network 502 to generate outputs which the discriminatorneural network 504 misclassifies as having been generated by the brainemulation neural network 204.

The brain emulation neural network 204 has an architecture based on agraph representing synaptic connectivity between neurons in the brain ofa biological organism. In some cases, the architecture of the brainemulation neural network 204 may be specified by the synapticconnectivity between neurons of a particular type in the brain, e.g.,neurons from the visual system or the olfactory system, as describedabove. The brain of the biological organism may be adapted byevolutionary pressures to be effective at solving certain tasks. Forexample, in contrast to many conventional computer vision techniques, abiological brain may process visual data to generate a robustrepresentation of the visual data that may be insensitive to factorssuch as the orientation and size of elements (e.g., objects)characterized by the visual data. The brain emulation neural network 204may also be effective at solving these (and other) tasks as a result ofhaving an architecture that matches the biological brain.

Processing data using the brain emulation neural network 204 may becomputationally expensive due to the complexity of the architecture ofthe brain emulation neural network 204. Generally, the student neuralnetwork 502 may have a less complex neural network architecture than thebrain emulation neural network 204. Therefore, training the studentneural network 502 to imitate the brain emulation neural network 204 mayenable the student neural network 502 to inherit the capacity of thebrain emulation neural network 204 to effectively solve certain tasks,while consuming fewer computational resources than the brain emulationneural network 204.

The student neural network 502 may be configured to process any of avariety of possible network inputs, e.g., image data, video data, audiodata, odor data, point cloud data (e.g., generated by a lidar or radarsensor), position and velocity data (e.g., characterizing the motion ofan agent), magnetic field data, or a combination thereof. The studentneural network 502 may be configured to generate any of a variety ofpossible network outputs, e.g., networks outputs that are embeddings ofthe corresponding network inputs. An embedding of a network input mayrefer to an ordered collection of numerical values (e.g., a vector ormatrix of numerical values) representing the network input. For example,an embedding of a network input may be a compact representation of thenetwork input that implicitly encodes features (e.g., semantic features)of the network input. The student neural network 502 may have anyappropriate neural network architecture, e.g., a feedforward neuralnetwork architecture or a recurrent neural network architecture. In oneexample, the student neural network 502 may have an architecturematching the AlexNet, which is described with reference to: A.Krizhevsky, I. Sutskever, G.E. Hinton: “ImageNet Classification withDeep Convolutional Neural Networks”, Advances in Neural InformationProcessing Systems 25 (NeurIPS), 2012.

The brain emulation neural network 204 is configured to process networkinputs having the same form as those processed by the student neuralnetwork 502, and to generate network outputs having the same form asthose generated by the student neural network 502. For example, thebrain emulation neural network 204 and the student neural network 502may both be configured to process 500 × 500 images to generate 50 × 1embeddings of the images. The form of the input layer and the outputlayer of the brain emulation neural network 204 may be adjusted asnecessary to enable the brain emulation neural network 204 toaccommodate network inputs and network outputs having a specifiedformat.

The discriminator neural network 504 is configured to process a networkinput to generate a discriminative score that classifies whether thenetwork input was generated by: (i) the student neural network 502, or(ii) the brain emulation neural network 204. For example, thediscriminative score may be a probability value (i.e., a numerical valuein the range [0,1]) representing a likelihood that the network input wasgenerated by the brain emulation neural network 204 rather than thestudent neural network 502. The discriminator neural network 504 canhave any appropriate neural network architecture that enables it toperform its described function. In one example, the discriminator neuralnetwork 504 may have an architecture specified by a sequence ofconvolutional layers followed by a fully-connected output layer.

The adversarial training system 500 uses a training engine 506 tojointly train the student neural network 502 and the discriminatorneural network 504 on a set of training data 508. The training data 508includes a set of network inputs that may be processed by the studentneural network 502 and the brain emulation neural network 204.

The training engine 506 may train the student neural network 502 by, ateach of multiple training iterations, sampling a “batch” (i.e., set) ofnetwork inputs from the training data 508 and processing the networkinputs using the student neural network 502 to generate correspondingoutputs 510. The training engine 506 may process the outputs 510generated by the student neural network 502 using the discriminatorneural network 504 to generate a corresponding discriminative score 512for each of the outputs 510 generated by the student neural network 502.The training engine 506 may then adjust the model parameters 514 of thestudent neural network 502 to optimize (e.g., minimize) an objectivefunction based on the discriminative scores 512. In one example, theobjective function L_(student) may be given by:

$L_{student} = {\sum\limits_{i = 1}^{N}{\log( {1 - D( S_{i} )} )}}$

where N is the number of network inputs in the current batch, S_(i) isthe student neural network output for the i-th network input in thecurrent batch, and D(S_(i)) is the discriminative score for S_(i).

To train the student neural network 502, the training engine 506 maydetermine gradients of the objective function with respect to the modelparameters of the student neural network (e.g., using backpropagationtechniques), and adjust the current values of the model parameters ofthe student neural network using the gradients. The training engine 506may adjust the current values of the model parameters of the studentneural network, e.g., using an RMSprop or Adam gradient descentoptimization technique. Generally, updating the model parameters of thestudent neural network to optimize the objective function may encouragethe student neural network to generate outputs that are misclassified bythe discriminator neural network as having been generated by the brainemulation neural network.

The training engine 506 may train the discriminator neural network 504by, at each of multiple training iterations, sampling a batch of networkinputs from the training data 508 and processing the network inputsusing the brain emulation neural network 204 to generate correspondingoutputs 516. The training engine 506 may process the outputs 516generated by the brain emulation neural network 204 using thediscriminator neural network 504 to generate correspondingdiscriminative scores 518. The training engine 506 may further sampleanother batch of network inputs from the training data 508 and processthe network inputs using the student neural network 502 to generatecorresponding outputs 510. The training engine 506 may process theoutputs 510 generated by the student neural network 502 using thediscriminator neural network 504 to generate correspondingdiscriminative scores 512. The training engine 506 may then adjust themodel parameters 514 of the discriminator neural network 504 to optimize(e.g., minimize) an objective function based on the discriminativescores (512, 518) generated by the discriminator neural network 504. Inone example, the objective function L_(discriminator) may be given by:

$L_{discriminator} = - {\sum\limits_{j = 1}^{M}{\log( {D( B_{j} )} ) - {\sum\limits_{i = 1}^{N}( {1 - D( S_{i} )} )}}}$

where M is the number of network inputs in the current batch processedby the brain emulation neural network, B_(j) is the brain emulationneural network output for the j-th network input processed by the brainemulation neural network, D(B_(j)) is the discriminative score forB_(j), N is the number of network inputs in the current batch processedby the student neural network, S_(i) is the student neural networkoutput for the i-th network input processed by the student neuralnetwork, and D(S_(i)) is the discriminative score for S_(i).

To train the discriminator neural network 504, the training engine 506may determine gradients of the objective function with respect to themodel parameters of the discriminator neural network (e.g., usingbackpropagation techniques), and adjust the current values of the modelparameters of the discriminator neural network using the gradients. Thetraining engine 506 may adjust the current values of the modelparameters of the discriminator neural network, e.g., using an RMSpropor Adam gradient descent optimization technique. Generally, updating themodel parameters of the discriminator neural network to optimize theobjective function may encourage the discriminator neural network todistinguish more accurately between outputs generated by the studentneural network and outputs generated by the brain emulation neuralnetwork.

The adversarial training system 500 may alternate between training thestudent neural network 502 and training the discriminator neural network504. For example, the adversarial training system 500 may train thestudent neural network 502 for one or more training iterations usingdiscriminative scores generated in accordance with the current values ofthe discriminator neural network parameters. The adversarial trainingsystem 500 may then train the discriminator neural network 504 for oneor more training iterations using outputs generated by the studentneural network in accordance with the current values of the studentneural network parameters, before reverting back to training thediscriminator neural network. In this manner, the student neural networkmay continuously learn to generate outputs that imitate the brainemulation neural network more effectively, and the discriminator neuralnetwork may continuously adapt to identify differences between outputsgenerated by the brain emulation neural network and outputs generated bythe student neural network.

Generally, the adversarial training system 500 is not required to trainthe parameter values of the brain emulation neural network during thejoint training of the student neural network 502 and the discriminatorneural network 504. Rather, the weight values corresponding to theconnections between the artificial neurons of the brain emulation neuralnetwork 204 may be randomly generated or derived from the synapticresolution image of the biological brain, as described above. Even inimplementations where the parameter values of the brain emulation neuralnetwork 204 are randomly generated, the behavior of the brain emulationneural network may still have desirable properties that result from thearchitecture of the brain emulation neural network (i.e., independentlyof the weight values).

After being trained to imitate the brain emulation neural network 204,the student neural network 502 can be used for any of a variety ofpurposes. In one example, the student neural network 502 may generateimage embeddings that are processed to perform object classification. Inanother example, the student neural network 502 may generate audio dataembeddings that are processed to perform speech recognition tasks. Inanother example, the student neural network may process sequences ofnetwork inputs to generate corresponding sequences of network outputsthat are used for navigation purposes, i.e., that are provided to anavigation system.

Generally, the adversarial training system 500 may be understood astraining the student neural network 502 to imitate the brain emulationneural network 204 by causing the student neural network 502 to generateoutputs having similar characteristics to the outputs generated by thebrain emulation neural network. For example, after being trained, thestudent neural network 502 and the brain emulation neural network 204may induce similar distributions over the space of possible networkoutputs. However, for any given network input, the student neuralnetwork 502 and the brain emulation neural network 204 may generatesubstantially different outputs. In contrast, the distillation trainingsystem which will be described in more detail next with reference toFIG. 6 may train the student neural network 502 to match the outputgenerated by the brain emulation neural network for each network input.

FIG. 6 shows an example distillation training system 600. Thedistillation training system 600 is an example of a system implementedas computer programs on one or more computers in one or more locationsin which the systems, components, and techniques described below areimplemented.

The distillation training system 600 trains a student neural network 602to process network inputs to generate network outputs that match thosegenerated by a brain emulation neural network 204 by processing the samenetwork inputs. Generally, both the student neural network 602 and thebrain emulation neural network 204 are configured to process a networkinput to generate a network output that includes a respective score foreach of multiple classes. A few examples follow.

In one example, the brain emulation neural network 204 may be configuredto generate a classification output that classifies the network inputinto a predefined number of possible categories. For example, thenetwork input may be an image, each category may specify a type ofobject (e.g., person, vehicle, building, and the like), and the brainemulation neural network 204 may classify an image into a category ifthe image depicts an object included in the category. As anotherexample, the network input may be an odor, each category may specify atype of odor, and the brain emulation neural network 204 may classify anodor into a category if the odor is of the type specified by thecategory.

In another example, the brain emulation neural network 204 may beconfigured to generate action selection outputs that can be used toselect actions to be performed by an agent interacting with anenvironment. For example, the action selection output may specify arespective score for each action in a set of possible actions that canbe performed by the agent, and the agent may select the action to beperformed by sampling an action in accordance with the action scores. Inone example, the agent may be a mechanical agent (e.g., a robot or anautonomous vehicle) interacting with a real-world environment to performa navigation task (e.g., reaching a goal location in the environment),and the actions performed by the agent cause the agent to navigatethrough the environment.

The brain emulation neural network 204 may have an architecture that isbased on a graph representing synaptic connectivity between neurons inthe brain of a biological organism. In some cases, the architecture ofthe brain emulation neural network 204 may be specified by the synapticconnectivity between neurons of a particular type in the brain, e.g.,neurons from the visual system or the olfactory system, as describedabove. The brain of the biological organism may be adapted byevolutionary pressures to be effective at solving certain tasks, and thebrain emulation neural network 204 may also be effective at solvingthese (and other) tasks as a result of having an architecture thatmatches the biological brain. However, processing data using the brainemulation neural network 204 may be computationally expensive, due tothe complexity of the architecture of the brain emulation neural network204. Generally, the student neural network 602 may have a less complexneural network architecture than the brain emulation neural network 204.Training the student neural network 602 to imitate the brain emulationneural network 204 may enable the student neural network 602 to inheritthe capacity of the brain emulation neural network 204 to effectivelysolve certain tasks, while consuming fewer computational resources thanthe brain emulation neural network 204.

Unlike in the adversarial training system described with reference toFIG. 5 , the distillation training system 600 requires that theparameter (weight) values of the brain emulation neural network betrained to perform the task, e.g., rather than being randomly generated.In some cases, the distillation training system 600 may train the brainemulation neural network prior to training the student neural network,while in other cases, the distillation training system 600 may jointlytrain the brain emulation neural network and the student neural network.

The distillation training system 600 may use a training engine 614 totrain the brain emulation neural network 204 on training data 604 thatincludes a set of training examples. Each training example may specify:(i) a training input, and (ii) a target output that should be generatedby the brain emulation neural network 204 by processing the traininginput. The training engine 614 may train the brain emulation neuralnetwork 204 on the training data 604 over multiple training iterations.At each training iteration, the training engine 614 may sample a batchof training examples, and process the training inputs specified by thetraining examples using the brain emulation neural network to generatecorresponding outputs. The training engine 614 may then determinegradients of an objective function measuring a similarity between: (i)the target outputs specified by the training examples, and (ii) theoutputs generated by the brain emulation neural network. The objectivefunction may be, e.g., a cross-entropy objective function. The trainingengine 614 may determine the gradients of the objective function withrespect to the model parameters of the brain emulation neural network,e.g., using backpropagation techniques. The training engine 614 may usethe gradients to adjust the current values of the model parameters ofthe brain emulation neural network, e.g., using an RMSprop or Adamgradient descent optimization technique.

The distillation training system 600 may use the training engine 614 totrain the model parameters 616 of the student neural network 602 overmultiple training iterations. At each training iteration, the trainingengine 614 may sample a batch of training inputs from the training data604, and process the training inputs using the brain emulation neuralnetwork 204 to generate corresponding outputs 606. The training engine614 may further process each sampled training input using the studentneural network 602 to generate a corresponding output 608. The trainingengine 614 may provide the student output 608 and the brain emulationoutput 606 for each training input to a similarity scoring engine 610 togenerate a similarity score 612. The similarity scores 612 may measure asimilarity between: (i) the student output 608, and (ii) the brainemulation output 606, for each training input. The similarity scoringengine 610 may compute the similarity score between a student output 608and a brain emulation output 606, e.g., as a cross-entropy similaritymeasure, or using any other appropriate similarity measure. The trainingengine 614 may determine gradients of an objective function that isbased on the respective similarity score 612 for each training example,e.g., using backpropagation techniques. The training engine 614 may thenuse the gradients to adjust the current model parameter values 616 ofthe student neural network 602, e.g., using an RMSprop or Adam gradientdescent optimization technique. In one example, the objective functionmay be given by:

$L_{distillation} = - {\sum\limits_{i = 1}^{N}{S( {B_{i},S_{i}} )}}$

where N is the number of training examples in the current batch, B_(i)is the brain emulation neural network output for the i-th trainingexample, S_(i) is the student output for the i-th training example, andS(•) is a similarity measure, e.g., a cross-entropy similarity measure.

Generally, the output generated by the brain emulation neural network204 by processing a training input specifies a respective “soft” scorefor each possible class, i.e., scores that may indicate respectivepositive probabilities that the training input may be in each ofmultiple classes. For example, the soft scores generated by the brainemulation neural network 204 for a training image may specify an 85%likelihood that the image depicts a truck, a 12% likelihood that theimage depicts a car, and various (small) positive likelihoods that theimage depicts each of multiple other objects. In contrast, the targetoutput for a training input specifies a “hard” score for each possibleclass, e.g., scores that indicate with 100% likelihood that an imagedepicts a truck, and 0% likelihoods that the image depicts otherpossible objects. Training the student neural network 602 to match thesoft outputs generated by the brain emulation neural network enables thestudent neural network to leverage the classification uncertaintyencoded in the soft outputs, and may therefore facilitate training ofthe student neural network more effectively than the hard targetoutputs.

After being trained by the distillation training system 600, the studentneural network may have a prediction accuracy comparable to that of thebrain emulation neural network, while having a substantially lesscomplex neural network architecture and therefore consuming fewercomputational resources than the brain emulation neural network.

FIG. 7 shows an example reservoir computing system 700. The reservoircomputing system 700 is an example of a system implemented as computerprograms on one or more computers in one or more locations in which thesystems, components, and techniques described below are implemented.

The reservoir computing system 700 includes a reservoir computing neuralnetwork 702 having two sub-networks: (i) a brain emulation neuralnetwork 204, and (ii) a prediction neural network 704. The reservoircomputing neural network 702 is configured to process a network input706 to generate a network output 708. More specifically, the brainemulation neural network 204 is configured to process the network input706 in accordance with a set of model parameters 710 of the brainemulation neural network 204 to generate an alternative representation712 of the network input 706. The prediction neural network 704 isconfigured to process the alternative representation 712 of the networkinput 706 in accordance with a set of model parameters 714 of theprediction neural network 704 to generate the network output 708.

The brain emulation neural network 204 may have an architecture that isbased on a graph representing synaptic connectivity between neurons inthe brain of a biological organism. In some cases, the architecture ofthe brain emulation neural network 204 may be specified by the synapticconnectivity between neurons of a particular type in the brain, e.g.,neurons from the visual system or the olfactory system, as describedabove. Generally, the brain emulation neural network 204 has a morecomplex neural network architecture than the prediction neural network704. In one example, the prediction neural network 704 may include onlyone neural network layer (e.g., a fully-connected layer) that processesthe alternative representation 712 of the network input 706 to generatethe network output 708.

In some cases, the brain emulation neural network 204 may have arecurrent neural network architecture, i.e., where the connections inthe architecture define one or more “loops.” More specifically, thearchitecture may include a sequence of components (e.g., artificialneurons, layers, or groups of layers) such that the architectureincludes a connection from each component in the sequence to the nextcomponent, and the first and last components of the sequence areidentical. In one example, two artificial neurons that are each directlyconnected to one another (i.e., where the first neuron provides itsoutput the second neuron, and the second neuron provides its output tothe first neuron) would form a recurrent loop. A recurrent brainemulation neural network may process a network input over multiple(internal) time steps to generate a respective alternativerepresentation 712 of the network input at each time step. Inparticular, at each time step, the brain emulation neural network mayprocess: (i) the network input, and (ii) any outputs generated by thebrain emulation neural network at the preceding time step, to generatethe alternative representation for the time step. The reservoircomputing neural network 702 may provide the alternative representationof the network input generated by the brain emulation neural network atthe final time step as the input to the prediction neural network 704.The number of time steps over which the brain emulation neural network204 processes a network input may be a predetermined hyper-parameter ofthe reservoir computing system 700.

In addition to processing the alternative representation 712 generatedby the output layer of the brain emulation neural network 204, theprediction neural network 704 may additionally process one or moreintermediate outputs of the brain emulation neural network 204. Anintermediate output refers to an output generated by a hidden artificialneuron of the brain emulation neural network, i.e., an artificial neuronthat is not included in the input layer or the output layer of the brainemulation neural network.

The reservoir computing system 700 includes a training engine 716 thatis configured to train the reservoir computing neural network 702.Training the reservoir computing neural network 702 from end-to-end(i.e., training both the model parameters 710 of the brain emulationneural network 204 and the model parameters 714 of the prediction neuralnetwork 704) may be difficult due to the complexity of the architectureof the brain emulation neural network. In particular, the brainemulation neural network may have a very large number of trainableparameters and may have a highly recurrent architecture (i.e., anarchitecture that includes loops, as described above). Therefore,training the reservoir computing neural network 702 from end-to-endusing machine learning training techniques may becomputationally-intensive and the training may fail to converge, e.g.,if the values of the model parameters of the reservoir computing neuralnetwork 702 oscillate rather than converging to fixed values. Even incases where the training of the reservoir computing neural network 702converges, the performance of the reservoir computing neural network 702(e.g., measured by prediction accuracy) may fail to achieve anacceptable threshold. For example, the large number of model parametersof the reservoir computing neural network 702 may overfit the limitedamount of training data.

Rather than training the entire reservoir computing neural network 702from end-to-end, the training engine 716 only trains the modelparameters 714 of the prediction neural network 704 while leaving themodel parameters 710 of the brain emulation neural network 204 fixedduring training. The model parameters 710 of the brain emulation neuralnetwork 204 may be determined before the training of the predictionneural network 704 based on the weight values of the edges in thesynaptic connectivity graph, as described above. Optionally, the weightvalues of the edges in the synaptic connectivity graph may betransformed (e.g., by additive random noise) prior to being used forspecifying model parameters 710 of the brain emulation neural network204. This training procedure enables the reservoir computing neuralnetwork 702 to take advantage of the highly complex and non-linearbehavior of the brain emulation neural network 204 in performingprediction tasks while obviating the challenges of training the brainemulation neural network 204.

The training engine 716 may train the reservoir computing neural network702 on a set of training data over multiple training iterations. Thetraining data may include a set of training examples, where eachtraining example specifies: (i) a training network input, and (ii) atarget network output that should be generated by the reservoircomputing neural network 702 by processing the training network input.

At each training iteration, the training engine 716 may sample a batchof training examples from the training data, and process the traininginputs specified by the training examples using the reservoir computingneural network 702 to generate corresponding network outputs 708. Inparticular, the reservoir computing neural network 702 processes eachnetwork input 706 in accordance with the static model parameter values710 of the brain emulation neural network 204 to generate an alternativerepresentation 712 of the network input 706. The reservoir computingneural network 702 then processes the alternative representation 712using the current model parameter values 714 of the prediction neuralnetwork 704 to generate the network output 708. The training engine 716adjusts the model parameter values 714 of the prediction neural network704 to optimize an objective function that measures a similaritybetween: (i) the network outputs 708 generated by the reservoircomputing neural network 702, and (ii) the target network outputsspecified by the training examples. The objective function may be, e.g.,a cross-entropy objective function, a squared-error objective function,or any other appropriate objective function.

To optimize the objective function, the training engine 716 maydetermine gradients of the objective function with respect to the modelparameters 714 of the prediction neural network 704, e.g., usingbackpropagation techniques. The training engine 716 may then use thegradients to adjust the model parameter values 714 of the predictionneural network, e.g., using any appropriate gradient descentoptimization technique, e.g.., an RMSprop or Adam gradient descentoptimization technique.

The training engine 716 may use any of a variety of regularizationtechniques during training of the reservoir computing neural network702. For example, the training engine 716 may use a dropoutregularization technique, such that certain artificial neurons of thebrain emulation neural network are “dropped out” (e.g., by having theiroutput set to zero) with a non-zero probability p > 0 each time thebrain emulation neural network processes a network input. Using thedropout regularization technique may improve the performance of thetrained reservoir computing neural network 702, e.g., by reducing thelikelihood of over-fitting. An example dropout regularization techniqueis described with reference to: N. Srivastava, et al.: “Dropout: asimple way to prevent neural networks from overfitting,” Journal ofMachine Learning Research 15 (2014) 1929-1958. As another example, thetraining engine 716 may regularize the training of the reservoircomputing neural network 702 by including a “penalty” term in theobjective function that measures the magnitude of the model parametervalues 714 of the prediction neural network 704. The penalty term maybe, e.g., an L₁ or L₂ norm of the model parameter values 714 of theprediction neural network 704.

In some cases, the values of the intermediate outputs of the brainemulation neural network 204 may have large magnitudes, e.g., as aresult from the parameter values of the brain emulation neural network204 being derived from the weight values of the edges of the synapticconnectivity graph rather than being trained. Therefore, to facilitatetraining of the reservoir computing neural network 702, batchnormalization layers may be included between the layers of the brainemulation neural network 204, which can contribute to limiting themagnitudes of intermediate outputs generated by the brain emulationneural network. Alternatively or in combination, the activationfunctions of the neurons of the brain emulation neural network may beselected to have a limited range. For example, the activation functionsof the neurons of the brain emulation neural network may be selected tobe sigmoid activation functions with range given by [0,1],

The reservoir computing neural network 702 may be configured to performany appropriate task. A few examples follow.

In one example, the reservoir computing neural network 702 may beconfigured to generate a classification output that classifies thenetwork input into a predefined number of possible categories. Forexample, the network input may be an image, each category may specify atype of object (e.g., person, vehicle, building, and the like), and thereservoir computing neural network 702 may classify an image into acategory if the image depicts an object included in the category. Asanother example, the network input may be an odor, each category mayspecify a type of odor (e.g., decomposing or not decomposing), and thereservoir computing neural network 702 may classify an odor into acategory if the odor is of the type specified by the category.

In another example, the reservoir computing neural network 702 may beconfigured to generate an action selection output that can be used toselect an action to be performed by an agent interacting with anenvironment. For example, the action selection output may specify arespective score for each action in a set of possible actions that canbe performed by the agent, and the agent may select the action to beperformed by sampling an action in accordance with the action scores. Inone example, the agent may be a mechanical agent interacting with areal-world environment to perform a navigation task (e.g., reaching agoal location in the environment), and the actions performed by theagent cause the agent to navigate through the environment.

After training, the reservoir computing neural network 702 may bedirectly applied to perform prediction tasks. However, deployment of thereservoir computing neural network in resource-constrained environments(e.g., mobile devices), or use of the reservoir computing neural networkin applications that require minimal latency, may be infeasible due tothe complexity of the architecture of the reservoir computing neuralnetwork. Therefore, the reservoir computing neural network may be usedto train a simpler “student” neural network, e.g., in a similar manneras described with reference to the adversarial training system of FIG. 5or the distillation training system of FIG. 6 .

FIG. 8A shows an example architecture search system 800. Thearchitecture search system 800 is an example of a system implemented ascomputer programs on one or more computers in one or more locations inwhich the systems, components, and techniques described below areimplemented.

The architecture search system 800 is configured to search a space ofpossible neural network architectures to identify a “task-specific”neural network architecture 216 that can be used to effectively performa machine learning task. The architecture search system 800 may “seed”(i.e., initialize) the search through the space of possible neuralnetwork architectures using a synaptic connectivity graph 202representing synaptic connectivity in the brain of a biologicalorganism. In particular, the architecture search system 800 may use thesynaptic connectivity graph 202 to derive a set of “candidate” graphs802, each of which can be mapped to a corresponding neural networkarchitecture, e.g., using the architecture mapping system described withreference to FIG. 3 . The architecture search system 800 may use anevaluation engine 804 to determine a quality measure 806 for eachcandidate graph 802 that characterizes the performance of the neuralnetwork architecture specified by the candidate graph on the machinelearning task. The architecture search system 800 may identify thebest-performing graph 808 based on the quality measures 806, and thenidentify the task-specific neural network architecture 216 as thearchitecture specified by the best-performing graph 808.

Generally, the performance of a neural network on a machine learningtask depends on the architecture of the neural network. The brain of abiological organism may be adapted by evolutionary pressures to beeffective at solving certain tasks, and therefore a neural networkhaving an architecture specified by a synaptic connectivity graphcorresponding to the brain may inherit the capacity to effectively solvetasks. By seeding the neural architecture search process using thesynaptic connectivity graph, the architecture search system 800 mayfacilitate the discovery of large numbers of biologically-inspiredneural network architectures, some of which may be particularlyeffective at performing certain machine learning tasks.

The synaptic connectivity graph 202 provided to the architecture searchsystem 800 may be derived directly from a synaptic resolution image ofthe brain of a biological organism, e.g., as described with reference toFIG. 2 . In some cases, the synaptic connectivity graph 202 may be asub-graph of a larger graph derived from a synaptic resolution image ofa brain, e.g., a sub-graph that includes neurons of a particular type,e.g., visual neurons, olfactory neurons, or memory neurons. An exampleprocess for identifying a sub-graph of a larger synaptic connectivitygraph is described with reference to FIG. 3 .

The architecture search system 800 may generate the set of candidategraphs 802 from the synaptic connectivity graph 202 using any of avariety of techniques. A few examples follow.

In one example, the architecture search system 800 may use a constraintsatisfaction system 810 to generate the set of candidate graphs 802 fromthe synaptic connectivity graph 202. To generate the candidate graphs802, the constraint satisfaction system 810 may process the synapticconnectivity graph 202 to determine values of a set of graph featurescharacterizing the synaptic connectivity graph 202. Graph featurescharacterizing a graph may include, e.g., the number of nodes in thegraph, the fraction of pairs of nodes in the graph that are connected byedges, and the average path length between pairs of nodes in the graph.The constraint satisfaction system 810 may use the values of the graphfeatures characterizing the synaptic connectivity graph 202 to generatea set of “constraints” on the candidate graphs 802. Each constraintcorresponds to a graph feature and specifies a target value or range oftarget values for the corresponding graph feature of each candidategraph 802. The constraint satisfaction system 810 may then generatecandidate graphs using a procedure defined by the constraints, e.g.,such that each candidate graph satisfies at least one of theconstraints. An example constraint satisfaction system 810 is describedin more detail with reference to FIG. 8B.

In another example, the architecture search system 800 may use anevolutionary system 812 to generate the set of candidate graphs 802 fromthe synaptic connectivity graph 202. The evolutionary system 812 maygenerate the candidate graphs 802 by “evolving” a population (i.e., aset) of graphs derived from the synaptic connectivity graph 202 overmultiple iterations (referred to herein as “evolutionary” iterations).The evolutionary system 812 may initialize the population of graphs,e.g., by “mutating” multiple copies of the synaptic connectivity graph202. Mutating a graph refers to making a random change to the graph,e.g., by randomly adding or removing edges or nodes from the graph.After initializing the population of graphs, the evolutionary system 812may change the population of graphs at each evolutionary iteration,e.g., by removing graphs, adding new graphs, or modifying the existinggraphs, based on the performance of the neural network architecturesspecified by the population of graphs. The evolutionary system 812 mayidentify the population of graphs after the final evolutionary iterationas the set of candidate graphs 802. An example evolutionary system 812is described in more detail with reference to FIG. 8C.

In another example, the architecture search system 800 may use anoptimization system 838 to generate the set of candidate graphs 802 fromthe synaptic connectivity graph 202. An example optimization system 838is described in more detail with reference to FIG. 8D.

The architecture search system 800 uses the evaluation engine 804 todetermine a respective quality measure 806 for each candidate graph 802.The evaluation engine 804 may determine the quality measure 806 for acandidate graph 802 based on a performance measure on a machine learningtask of a neural network having the neural network architecturespecified by the candidate graph 802. The architecture mapping system300 may map each candidate graph 802 to a corresponding neural networkarchitecture, e.g., using the architecture mapping system described withreference to FIG. 3 . The machine learning task may be, e.g., aclassification task, a regression task, or any other appropriate task.

The evaluation engine 804 may measure the performance of a neuralnetwork on a machine learning task, e.g., by training the neural networkon a set of training data 814, and then evaluating the performance ofthe trained neural network on a set of validation data 816. Both thetraining data 814 and the validation data 816 may include trainingexamples, where each training example specifies: (i) a training input,and (ii) a target output that should be generated by processing thetraining input. In determining the performance measure of a neuralnetwork, the evaluation engine 804 trains the neural network on thetraining data 814, but reserves the validation data 816 for evaluatingthe performance of the trained neural network (i.e., by not training theneural network on the validation data 816). The evaluation engine 804may evaluate the performance of the trained neural network on thevalidation data 816, e.g., by using an objective function to measure asimilarity between: (i) the target outputs specified by the validationdata, and (ii) the outputs generated by the trained neural network. Theobjective function may be, e.g., a cross-entropy objective function (inthe case of a classification task) or a squared-error objective function(in the case of a regression task).

In some cases, the evaluation engine 804 may determine the qualitymeasure 806 for a candidate graph 802 by indirectly measuring theperformance of the neural network architecture specified by thecandidate graph on the machine learning task. For example, to determinethe quality measure 806 for a candidate graph 802, the evaluation engine804 may instantiate a corresponding reservoir computing neural networkas described with reference to FIG. 7 . In particular, the reservoircomputing neural network may include: (i) a “reservoir” sub-networkhaving an architecture specified by the candidate graph to process theinput to the reservoir computing neural network to generate thealternative representation of the input, and (ii) a prediction neuralnetwork to process the alternative representation. The evaluation engine804 may train the reservoir computing neural network on the trainingdata 814, i.e., by training the parameter values of the predictionneural network while leaving the parameter values of the reservoirsub-network fixed, as described with reference to FIG. 7 . Theevaluation engine 804 may then determine the quality measure 806 for thecandidate graph 802 based on a performance measure of the trainedreservoir computing neural network evaluated on the validation data 816.Determining the quality measures 806 for the candidate graphs 802 inthis manner may less computationally intensive than directly trainingneural networks having architectures specified by the candidate graphs802 on the training data 814.

In determining the quality measure 806 for a candidate graph 802, theevaluation engine 804 may take other factors into consideration inaddition to the performance measure of the neural network architecturespecified by the candidate graph 802 on the machine learning task. Forexample, the evaluation engine 804 may further determine the qualitymeasure 806 for a candidate graph 802 based on the computationalresource consumption of a neural network having the neural networkarchitecture specified by the candidate graph. The computationalresource consumption corresponding to a neural network architecture maybe determined based on, e.g.: (i) the memory required to store dataspecifying the architecture, and (ii) the number of arithmeticoperations performed by a neural network having the architecture togenerate a network output. In one example, the evaluation engine 804 maydetermine the quality measure 806 of each candidate graph as a linearcombination of: (i) a performance measure of the neural networkarchitecture on the machine learning task, and (ii) a measure of thecomputational resource consumption induced by the neural networkarchitecture.

The architecture search system 800 may identify a best-performing graph808 based on the quality measures 806. For example, the architecturesearch system 800 may identify the best-performing graph 808 as thecandidate graph 802 with the highest quality measure 806.

After identifying the best-performing graph 808 from the set ofcandidate graphs 802, the architecture search system 800 may provide theneural network architecture 216 specified by the best-performing graph808 for use in performing the machine learning task.

FIG. 8B shows an example constraint satisfaction system 810. Theconstraint satisfaction system 810 is an example of a system implementedas computer programs on one or more computers in one or more locationsin which the systems, components, and techniques described below areimplemented.

The constraint satisfaction system 810 is configured to generate a setof candidate graphs 802 based on “constraints” derived from the valuesof graph features characterizing the synaptic connectivity graph 202.The candidate graphs 802 generated by the constraint satisfaction system810 each specify a neural network architecture that may be provided tothe architecture search system described with reference to FIG. 8A.

The constraint satisfaction system 810 generates the candidate graphs802 from the synaptic connectivity graph 202 using a feature generationengine 818 and a graph update engine 820, each of which will bedescribed in more detail next.

The feature generation engine 818 is configured to process the synapticconnectivity graph 202 to determine the values of one or more graphfeatures 822 of the synaptic connectivity graph 202, e.g., thatcharacterize various aspects of the structure of the synapticconnectivity graph 202. A few examples of graph features follow.

In one example, the feature generation engine 818 may determine a graphfeature value 822 that specifies the number of nodes in the synapticconnectivity graph 202.

In another example, the feature generation engine 818 may determine agraph feature value 822 that specifies the number of edges in thelargest cluster in a two-dimensional array representing the synapticconnectivity graph 202. A cluster in a two-dimensional arrayrepresenting a graph may refer to a contiguous region of the array suchthat at least a threshold fraction of the components in the region havea value indicating that an edge exists between the pair of nodescorresponding to the component, as described with reference to FIG. 3 .

In another example, the feature generation engine 818 may determine agraph feature value 822 that specifies the number of clusters in thetwo-dimensional array representing the synaptic connectivity graph 202that include a number of edges that is within a predefined range ofvalues, e.g., the range [5,10].

In another example, the feature generation engine 818 may determinegraph feature values 822 that specify, for each of multiple predefinedranges of values, the number of clusters in the two-dimensional arrayrepresenting the synaptic connectivity graph that include a number ofedges that is within the range of values. The predefined ranges ofvalues may be, e.g.: {[1,10], [10,100], [100, ∞)}.

In another example, the feature generation engine 818 may determine agraph feature value 822 that specifies the average path length betweennodes in the synaptic connectivity graph 202.

In another example, the feature generation engine 818 may determine agraph feature value 822 that specifies the maximum path length betweennodes in the synaptic connectivity graph 202.

In another example, the feature generation engine 818 may determine agraph feature value 822 that specifies the fraction of node pairs in thesynaptic connectivity graph 202 (i.e., where a node pair specifies afirst node and a second node in the synaptic connectivity graph 202)that are connected by an edge.

In another example, the feature generation engine 818 may determine agraph feature value 822 that specifies the fraction of nodes in thesynaptic connectivity graph 202 having the property that the synapticconnectivity graph 202 includes an edge that connects the node toitself.

The constraint satisfaction system 810 determines one or moreconstraints 824 from the graph features values 822 characterizing thesynaptic connectivity graph 202. Each constraint corresponds to arespective graph feature and specifies a target value or a range oftarget values of the graph feature for the candidate graphs 802. A fewexamples of determining constraints from the graph feature values 822characterizing the synaptic connectivity graph 202 are described next.

In one example, the constraint satisfaction system 810 may determine aconstraint specifying a target value for a graph feature for thecandidate graphs 802 that matches the value of the graph feature for thesynaptic connectivity graph 202. For example, if the value of the graphfeature specifying the number of nodes in the synaptic connectivitygraph 202 is n, then the constraint satisfaction system 810 maydetermine the target value of the graph feature specifying the number ofnodes in each candidate graph 802 to be n.

As another example, the constraint satisfaction system 810 may determinea constraint specifying a range of target values for a graph feature forthe candidate graphs 802, where the range of target values includes thevalue of the graph feature for the synaptic connectivity graph 202. Inone example, the value of the graph feature specifying the fraction ofnode pairs in the synaptic connectivity graph 202 that are connected byan edge may be p ∈ (0,1). In this example, the constraint satisfactionsystem 810 may determine the target range of values of the graph featurespecifying the fraction of node pairs in each candidate graph 802 thatare connected by an edge to be [p - ∈, p + ∈] ∩ [0,1], where ∈ > 0.

The graph update engine 820 uses the constraints 824 to guide aprocedure for randomly generating candidate graphs 802, e.g., to causeeach of the candidate graphs 802 to satisfy at least one of theconstraints 824. For example, the graph update engine 820 may generate acandidate graph 802 by iteratively updating an “initial” graph 826,e.g., by adding or removing nodes or edges from the initial graph 826 ateach of one or more iterations. The initial graph 826 may be, e.g., adefault (predefined) graph, or a randomly generated graph. At eachiteration, the graph update engine 820 may update the current graph tocause the current graph to satisfy a corresponding constraint 824. Forexample, the constraints 824 may be associated with a predefined linearordering

{C_(i)}_(i = 0)^(N − 1)

(i.e., where each C_(i) denotes a constraint), and at the j-thiteration, the graph update engine 820 may update the current graph tocause it to satisfy constraint C_((j) _(mod) _(N)). Put another way: atthe first iteration, the graph update engine 820 may update the initialgraph to cause it to satisfy the first constraint; at the seconditeration, the graph update engine 820 may update the current graph tocause it to satisfy the second constraint; and so on. After updating thecurrent graph to cause it to satisfy the final constraint, the graphupdate engine 820 may loop back to the first constraint. After a finaliteration (e.g., of a predefined number of iterations), the graph updateengine 820 may output the current graph as a candidate graph 802.

At any given iteration, the graph update engine 820 may update thecurrent graph to satisfy a corresponding constraint 824 using aprocedure that involves some randomness. In one example, the graphupdate engine 820 may update the current graph to satisfy a constraintspecifying that the fraction of node pairs in the graph that areconnected by an edge be p ∈ (0,1). In this example, the graph updateengine 820 may randomly add or remove edges from the current graph untilthe constraint is satisfied. In another example, the graph update engine820 may update the current graph to satisfy a constraint specifying thatthe graph include N clusters that each have a number of edges that isincluded in the interval [A, B]. For convenience, this example willassume that the current graph is a default graph that does not yetinclude any edges. The graph update engine 820 may randomly select Nlocations in a representation of the graph as a two-dimensional arrayhaving value 0 in each component, e.g., by sampling N locations from auniform distribution over the array. For each of the N sampled locationsin the array, the graph update engine 820 may identify a contiguousregion around the location that includes a number of components in therange [A, B], and then set each component in the contiguous region tohave value 1 (i.e., indicating an edge).

In some cases, a candidate graph 802 generated by the graph updateengine 820 may not satisfy all of the constraints. In particular, at oneor more iterations during generation of the candidate graph, updatingthe current graph to cause it to satisfy a corresponding constraint 824may have resulted in the updated graph violating one or more otherconstraints.

FIG. 8C shows an example evolutionary system 812. The evolutionarysystem 812 is an example of a system implemented as computer programs onone or more computers in one or more locations in which the systems,components, and techniques described below are implemented.

The evolutionary system 812 is configured to generate a set of candidategraphs 802 from the synaptic connectivity graph 202 by evolving apopulation (i.e., set) of graphs 828 derived from the synapticconnectivity graph 202 over multiple evolutionary iterations. Thecandidate graphs 802 generated by the evolutionary system 812 eachspecify a neural network architecture that may be provided to thearchitecture search system described with reference to FIG. 8A.

At each evolutionary iteration, the evolutionary system 812 may adaptthe population of graphs 828 by removing one or more graphs from thepopulation 828, adding one or more graphs to the population 828, orchanging one or more graphs in the population 828. As will be describedin more detail below, the changes applied to the population of graphs ateach iteration include an element of randomness and are intended toincrease the quality measures of the graphs in the population 828. Aftera final evolutionary iteration, the evolutionary system 812 may providethe current population of graphs 828 as the set of candidate graphs 802.

Prior to the first evolutionary iteration, the evolutionary system 812may initialize the population 828 based on the synaptic connectivitygraph 202. For example, to initialize the population 828, theevolutionary system 812 may generate multiple copies of the synapticconnectivity graph 202, and “mutate” (i.e., modify) each copy of thesynaptic connectivity graph 202 to generate a mutated graph which isthen added to the initial population 828. The evolutionary system 812may mutate a graph by applying one or more random modifications to thegraph. The random modifications may include, e.g., adding or removingedges between randomly selected pairs of nodes in the graph, or addingrandom “noise” values (e.g., sampled from a predefined probabilitydistribution) to the weight values associated with the edges of thegraph.

At each evolutionary iteration, the sampling engine 830 may select(e.g., randomly sample) a set of current graphs 832 from the populationof graphs 828. The evolutionary system 812 may use an evaluation engine804 (e.g., as described with reference to FIG. 8A) to determine arespective quality measure 834 corresponding to each of the sampledgraphs 832. The quality measure for a graph may be based on aperformance measure on a machine learning task of a neural networkhaving the neural network architecture specified by the graph.

The population update engine 836 determines how the population of graphs828 should be updated at the current evolutionary iteration based on thequality measures 34 of the sampled graphs 832. For example, thepopulation update engine 836 may remove any sampled graphs 832 havingquality measures 34 that are below a threshold value. As anotherexample, for sampled graphs 832 having quality measures that are above athreshold value, the population update engine 836 may: (i) maintain thesampled graphs 832 in the population 828, and (ii) add randomly mutated(i.e., modified) copies of the sampled graphs 832 to the population 828.

Iteratively adapting the population of graphs 828 in this mannersimulates an evolutionary process by which graphs having desirabletraits (e.g., that result in higher quality measures) are propagated andmutated in the population, and graphs having undesirable traits (i.e.,that result in low quality measures) are removed from the population.Initializing the population of graphs 828 using the synapticconnectivity graph 202 may facilitate the evolution ofbiologically-inspired graphs specifying neural network architectures areeffective at performing machine learning tasks.

FIG. 8D shows an example optimization system 838. The optimizationsystem 838 is an example of a system implemented as computer programs onone or more computers in one or more locations in which the systems,components, and techniques described below are implemented.

The optimization system 838 generates candidate graphs 802 using a graphgeneration engine 840. The graph generation engine 840 is configured toprocess the synaptic connectivity graph 202 in accordance with a set ofgraph generation parameters 842 to generate an output graph 844 that isadded to the set of candidate graphs 802. The optimization system 838iteratively optimizes the parameters 842 of the graph generation engine840 using an optimization engine 846 to increase the quality measures848 of the output graphs 844 generated by the graph generation engine840, as will be described in more detail below.

The parameters 842 of the graph generation engine 840 specifytransformation operations that are applied to the synaptic connectivitygraph 202 to generate an output graph 844. The graph generation engine840 may generate the output graph 844 by applying transformationoperations to a representation of the synaptic connectivity graph 202 asa two-dimensional array of numerical values. As described above, a graphmay be represented as a two-dimensional array of numerical values with anumber of rows and columns equal to the number of nodes in the graph.The component of the array at position (i,j) may have value 1 if thegraph includes an edge pointing from node i to node j, and value 0otherwise. In one example, as part of generating an output graph 844,the graph generation engine 840 may apply a convolutional filteringoperation specified by a filtering kernel to the array representing thesynaptic connectivity graph 202. In this example, the graph generationparameters 842 may specify the components of a matrix defining thefiltering kernel. In another example, as part of generating an outputgraph 844, the graph generation engine 840 may apply a “shifting”operation to the array representing the synaptic connectivity graph 202,e.g., such that each the value in each component of the array istranslated “left”, “right”, “up”, or “down”. Components that are shiftedoutside the bounds of the array may be wrapped around the opposite sideof the array. In this example, the graph generation parameters 842 mayspecify the direction and magnitude of the shifting operation. Inanother example, as part of generating an output graph 844, the graphgeneration engine 840 may remove one or more nodes from the synapticconnectivity graph, e.g., such that the output graph is a sub-graph ofthe synaptic connectivity graph. In this example, the graph generationparameters 842 may specify the nodes to be removed from the synapticconnectivity graph 202 (e.g., the graph generation parameters 842 mayspecify the indices of the nodes to be removed from the synapticconnectivity graph 202).

At each of multiple iterations, the graph generation engine 840processes the synaptic connectivity graph 202 in accordance with thecurrent values of the graph generation parameters 842 to generate anoutput graph 844 which may then be added to the set of candidate graphs802. The optimization system 838 determines a quality measure 848 of theoutput graph 844 using an evaluation engine 804 (e.g., as described withreference to FIG. 8A), and then provides the quality measure 848 of theoutput graph 844 to the optimization engine 846.

The optimization engine 846 is configured to process the qualitymeasures 848 of the output graphs 844 to determine adjustments to thecurrent values of the graph generation parameters to encourage thegeneration of output graphs with higher quality measures. Prior to thefirst iteration, the values of the graph generation parameters 842 maybe set to default values or randomly initialized. The optimizationengine 846 may use any appropriate optimization technique, e.g., a“black-box” optimization technique that does not rely on computinggradients of the transformation operations applied by the graphgeneration engine 840. Examples of black-box optimization techniqueswhich may be implemented by the optimization engine 846 are describedwith reference to: Golovin, D., Solnik, B., Moitra, S., Kochanski, G.,Karro, J., & Sculley, D.: “Google vizier: A service for black-boxoptimization,” In Proceedings of the 23rd ACM SIGKDD InternationalConference on Knowledge Discovery and Data Mining, pp.1487-1495 (2017).

After the final iteration, the optimization system 838 may provide thecandidate graphs 802 for use by the architecture search system 800described with reference to FIG. 8A.

FIG. 9 is a flow diagram of an example process 900 for generating abrain emulation neural network. For convenience, the process 900 will bedescribed as being performed by a system of one or more computerslocated in one or more locations.

The system obtains a synaptic resolution image of at least a portion ofa brain of a biological organism (902).

The system processes the image to identify: (i) neurons in the brain,and (ii) synaptic connections between the neurons in the brain (904).

The system generates data defining a graph representing synapticconnectivity between the neurons in the brain (906). The graph includesa set of nodes and a set of edges, where each edge connects a pair ofnodes. The system identifies each neuron in the brain as a respectivenode in the graph, and each synaptic connection between a pair ofneurons in the brain as an edge between a corresponding pair of nodes inthe graph.

The system determines an artificial neural network architecturecorresponding to the graph representing the synaptic connectivitybetween the neurons in the brain (908).

The system processes a network input using an artificial neural networkhaving the artificial neural network architecture to generate a networkoutput (910).

FIG. 10 is a flow diagram of an example process 1000 for determining anartificial neural network architecture corresponding to a sub-graph of asynaptic connectivity graph. For convenience, the process 1000 will bedescribed as being performed by a system of one or more computerslocated in one or more locations. For example, an architecture mappingsystem, e.g., the architecture mapping system 300 of FIG. 3 ,appropriately programmed in accordance with this specification, canperform the process 1000.

The system obtains data defining a graph representing synapticconnectivity between neurons in a brain of a biological organism (1002).The graph includes a set of nodes and edges, where each edge connects apair of nodes. Each node corresponds to a respective neuron in the brainof the biological organism, and each edge connecting a pair of nodes inthe graph corresponds to a synaptic connection between a pair of neuronsin the brain of the biological organism.

The system determines, for each node in the graph, a respective set ofone or more node features characterizing a structure of the graphrelative to the node (1004).

The system identifies a sub-graph of the graph (1006). In particular,the system selects a proper subset of the nodes in the graph forinclusion in the sub-graph based on the node features of the nodes inthe graph.

The system determines an artificial neural network architecturecorresponding to the sub-graph of the graph (1008).

FIG. 11 is a flow diagram of an example process 1100 for adversarialtraining of a student neural network using a brain emulation neuralnetwork. For convenience, the process 1100 will be described as beingperformed by a system of one or more computers located in one or morelocations. For example, an adversarial training system, e.g., theadversarial training system 500 of FIG. 5 , appropriately programmed inaccordance with this specification, can perform the process 1100.

The system processes a training input using the student neural networkto generate an output for the training input (1102).

The system processes the student neural network output using adiscriminative neural network to generate a discriminative score for thestudent neural network output (1104). The discriminative neural networkis trained to process a network input to generate a discriminative scorethat characterizes a prediction for whether the network input wasgenerated using: (i) the student neural network, or (ii) a brainemulation neural network. The brain emulation neural network has aneural network architecture that is specified by a graph representingsynaptic connectivity between neurons in a brain of a biologicalorganism. The graph has a set of nodes and a set of edges, where eachedge connects a pair of nodes. Each node corresponds to a respectiveneuron in the brain of the biological organism, and each edge connectinga pair of nodes in the graph corresponds to a synaptic connectionbetween a pair of neurons in the brain of the biological organism.

The system adjusts current values of the student neural networkparameters using gradients of an objective function that depends on thediscriminative score for the student neural network output (1106).Adjusting the current values of the student neural network parametersusing gradients of an objective function encourages the student neuralnetwork to generate outputs that are more likely to be misclassified bythe discriminative neural network as having been generated by the brainemulation neural network.

FIG. 12 is a flow diagram of an example process 1200 for distillationtraining of a student neural network using a brain emulation neuralnetwork. For convenience, the process 1200 will be described as beingperformed by a system of one or more computers located in one or morelocations. For example, a distillation training system, e.g., thedistillation training system 600 of FIG. 6 , appropriately programmed inaccordance with this specification, can perform the process 1200.

The system processes a training input using the student neural networkto generate a student neural network output that includes a respectivescore for each of multiple classes (1202).

The system processes the training input using a brain emulation neuralnetwork to generate a brain emulation neural network output thatincludes a respective score for each of multiple classes (1204). Thebrain emulation neural network has a neural network architecture that isspecified by a graph representing synaptic connectivity between neuronsin a brain of a biological organism. The graph has a set of nodes and aset of edges, where each edge connects a pair of nodes. Each nodecorresponds to a respective neuron in the brain of the biologicalorganism, and each edge connecting a pair of nodes in the graphcorresponds to a synaptic connection between a pair of neurons in thebrain of the biological organism.

The system determines a similarity measure between: (i) the studentneural network output for the training input, and (ii) the brainemulation neural network output for the training input (1206).

The system adjusts the current values of the student neural networkparameters using gradients of an objective function that depends on thesimilarity measure between: (i) the student neural network output forthe training input, and (ii) the brain emulation neural network outputfor the training input (1208).

FIG. 13 is a flow diagram of an example process 1300 for processing datausing a reservoir computing neural network that includes: (i) a brainemulation sub-network, and (ii) a prediction sub-network. Forconvenience, the process 1300 will be described as being performed by asystem of one or more computers located in one or more locations. Forexample, a reservoir computing system, e.g., the reservoir computingsystem 700 of FIG. 7 , appropriately programmed in accordance with thisspecification, can perform the process 1300.

The system receives a network input to the processed by the reservoircomputing neural network (1302).

The system processes the network input using the brain emulationsub-network to generate an alternative representation of the networkinput (1304). The system determines the values of the brain emulationsub-network parameters before the reservoir computing neural network istrained and holds them fixed them during training of the reservoircomputing neural network. The brain emulation sub-network has a neuralnetwork architecture that is specified by a graph representing synapticconnectivity between neurons in a brain of a biological organism. Thegraph has a set of nodes and a set of edges, where each edge connects apair of nodes. Each node corresponds to a respective neuron in the brainof the biological organism, and each edge connecting a pair of nodes inthe graph corresponds to a synaptic connection between a pair of neuronsin the brain of the biological organism.

The system processes the alternative representation of the network inputusing the prediction sub-network to generate the network output (i.e.,of the reservoir computing neural network) (1306). The system adjuststhe values of the prediction sub-network parameters during training ofthe reservoir computing neural network.

FIG. 14 is a flow diagram of an example process for seeding a neuralarchitecture search procedure using a synaptic connectivity graph. Forconvenience, the process 1400 will be described as being performed by asystem of one or more computers located in one or more locations. Forexample, an architecture search system, e.g., the architecture searchsystem 800 of FIG. 8A, appropriately programmed in accordance with thisspecification, can perform the process 1400.

The system obtains data defining a synaptic connectivity graphrepresenting synaptic connectivity between neurons in a brain of abiological organism (1402). The synaptic connectivity graph has a set ofnodes and edges, where each edge connects a pair of nodes, each nodecorresponds to a respective neuron in the brain of the biologicalorganism. Each edge connecting a pair of nodes in the synapticconnectivity graph corresponds to a synaptic connection between a pairof neurons in the brain of the biological organism.

The system generates data defining a set of candidate graphs based onthe synaptic connectivity graph (1404).

The system determines, for each candidate graph, a performance measureon a machine learning task of a neural network having a neural networkarchitecture that is specified by the candidate graph (1406).

The system selects a final neural network architecture for performingthe machine learning task based on the performance measures (1408).

FIG. 15 is a block diagram of an example computer system 1500 that canbe used to perform operations described previously. The system 1500includes a processor 1510, a memory 1520, a storage device 1530, and aninput/output device 1540. Each of the components 1510, 1520, 1530, and1540 can be interconnected, for example, using a system bus 1550. Theprocessor 1510 is capable of processing instructions for executionwithin the system 1500. In one implementation, the processor 1510 is asingle-threaded processor. In another implementation, the processor 1510is a multi-threaded processor. The processor 1510 is capable ofprocessing instructions stored in the memory 1520 or on the storagedevice 1530.

The memory 1520 stores information within the system 1500. In oneimplementation, the memory 1520 is a computer-readable medium. In oneimplementation, the memory 1520 is a volatile memory unit. In anotherimplementation, the memory 1520 is a non-volatile memory unit.

The storage device 1530 is capable of providing mass storage for thesystem 1500. In one implementation, the storage device 1530 is acomputer-readable medium. In various different implementations, thestorage device 1530 can include, for example, a hard disk device, anoptical disk device, a storage device that is shared over a network bymultiple computing devices (for example, a cloud storage device), orsome other large capacity storage device.

The input/output device 1540 provides input/output operations for thesystem 1500. In one implementation, the input/output device 1540 caninclude one or more network interface devices, for example, an Ethernetcard, a serial communication device, for example, and RS-232 port,and/or a wireless interface device, for example, and 802.11 card. Inanother implementation, the input/output device 1540 can include driverdevices configured to receive input data and send output data to otherinput/output devices, for example, keyboard, printer and display devices1560. Other implementations, however, can also be used, such as mobilecomputing devices, mobile communication devices, and set-top boxtelevision client devices.

Although an example processing system has been described in FIG. 15 ,implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in other types ofdigital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.

This specification uses the term “configured” in connection with systemsand computer program components. For a system of one or more computersto be configured to perform particular operations or actions means thatthe system has installed on it software, firmware, hardware, or acombination of them that in operation cause the system to perform theoperations or actions. For one or more computer programs to beconfigured to perform particular operations or actions means that theone or more programs include instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the operations oractions.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisspecification can be implemented as one or more computer programs, i.e.,one or more modules of computer program instructions encoded on atangible non-transitory storage medium for execution by, or to controlthe operation of, data processing apparatus. The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them. Alternatively or in addition, the programinstructions can be encoded on an artificially-generated propagatedsignal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus.

The term “data processing apparatus” refers to data processing hardwareand encompasses all kinds of apparatus, devices, and machines forprocessing data, including by way of example a programmable processor, acomputer, or multiple processors or computers. The apparatus can alsobe, or further include, special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application-specificintegrated circuit). The apparatus can optionally include, in additionto hardware, code that creates an execution environment for computerprograms, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program, which may also be referred to or described as aprogram, software, a software application, an app, a module, a softwaremodule, a script, or code, can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages; and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A program may, but neednot, correspond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data, e.g., one or morescripts stored in a markup language document, in a single file dedicatedto the program in question, or in multiple coordinated files, e.g.,files that store one or more modules, sub-programs, or portions of code.A computer program can be deployed to be executed on one computer or onmultiple computers that are located at one site or distributed acrossmultiple sites and interconnected by a data communication network.

In this specification the term “engine” is used broadly to refer to asoftware-based system, subsystem, or process that is programmed toperform one or more specific functions. Generally, an engine will beimplemented as one or more software modules or components, installed onone or more computers in one or more locations. In some cases, one ormore computers will be dedicated to a particular engine; in other cases,multiple engines can be installed and running on the same computer orcomputers.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby special purpose logic circuitry, e.g., an FPGA or an ASIC, or by acombination of special purpose logic circuitry and one or moreprogrammed computers.

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors or both, or any other kindof central processing unit. Generally, a central processing unit willreceive instructions and data from a read-only memory or a random accessmemory or both. The essential elements of a computer are a centralprocessing unit for performing or executing instructions and one or morememory devices for storing instructions and data. The central processingunit and the memory can be supplemented by, or incorporated in, specialpurpose logic circuitry. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto-optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device, e.g., a universalserial bus (USB) flash drive, to name just a few.

Computer-readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser’s device in response to requests received from the web browser.Also, a computer can interact with a user by sending text messages orother forms of message to a personal device, e.g., a smartphone that isrunning a messaging application, and receiving responsive messages fromthe user in return.

Data processing apparatus for implementing machine learning models canalso include, for example, special-purpose hardware accelerator unitsfor processing common and compute-intensive parts of machine learningtraining or production, i.e., inference, workloads.

Machine learning models can be implemented and deployed using a machinelearning framework, e.g., a TensorFlow framework, a Microsoft CognitiveToolkit framework, an Apache Singa framework, or an Apache MXNetframework.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface, a web browser, or anapp through which a user can interact with an implementation of thesubject matter described in this specification, or any combination ofone or more such back-end, middleware, or front-end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data, e.g., an HTML page, to a userdevice, e.g., for purposes of displaying data to and receiving userinput from a user interacting with the device, which acts as a client.Data generated at the user device, e.g., a result of the userinteraction, can be received at the server from the device.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what may be claimed, but rather asdescriptions of features that may be specific to particular embodimentsof particular inventions. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially be claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings and recited inthe claims in a particular order, this should not be understood asrequiring that such operations be performed in the particular ordershown or in sequential order, or that all illustrated operations beperformed, to achieve desirable results. In certain circumstances,multitasking and parallel processing may be advantageous. Moreover, theseparation of various system modules and components in the embodimentsdescribed above should not be understood as requiring such separation inall embodiments, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In some cases, multitasking and parallel processing may beadvantageous.

1. (canceled)
 2. A method performed by one or more data processingapparatus, the method comprising: obtaining a three-dimensional (3D)synaptic resolution image of at least a portion of a brain of abiological organism; processing the 3D synaptic resolution image togenerate data defining a set of weight values that jointly parameterizean artificial neural network architecture; generating an artificialneural network that has the artificial neural network architecture andthat is parametrized by the set of weight values derived from the 3Dsynaptic resolution image of the brain; and processing a network inputusing the artificial neural network, in accordance with the set ofweight values derived from the 3D synaptic resolution image of thebrain, to generate a network output.
 3. The method of claim 2, whereinprocessing the 3D synaptic resolution image to generate data definingthe set of weight values that joint parametrize the artificial neuralnetwork architecture comprises: processing the 3D synaptic resolutionimage of the brain to identify a plurality of synaptic connectionsdepicted in the 3D synaptic resolution image of the brain; andprocessing the 3D synaptic resolution image of the brain to generate arespective biological weight value for each of the plurality of synapticconnections depicted in the 3D synaptic resolution image of the brain.4. The method of claim 3, wherein each of the plurality of synapticconnections is between a respective first neuron and a respective secondneuron in the brain of the biological organism, and wherein processingthe 3D synaptic resolution image of the brain to generate a respectivebiological weight value for each of the plurality of synapticconnections depicted in the 3D synaptic resolution image of the braincomprises, for each of the plurality of synaptic connections:determining: (i) a first tolerance region in the 3D synaptic resolutionimage around a first neuron corresponding to the synaptic connection,and (ii) a second tolerance region in the 3D synaptic resolution imagearound a second neuron corresponding to the synaptic connection; anddetermining the biological weight value for the synaptic connectionbased on an area of overlap between the first tolerance region and thesecond tolerance region.
 5. The method of claim 2, further comprisingprocessing the 3D synaptic resolution image of the brain to generate theartificial neural network architecture.
 6. The method of claim 5,wherein processing the 3D synaptic resolution image of the brain togenerate the artificial neural network architecture comprises:processing the 3D synaptic resolution image of the brain to identify aplurality of biological neurons depicted in the 3D synaptic resolutionimage; and instantiating a respective artificial neuron in theartificial neural network architecture corresponding to each of theplurality of biological neurons.
 7. The method of claim 6, furthercomprising: processing the 3D synaptic resolution image of the brain toidentify a plurality of synaptic connections depicted in the 3D synapticresolution image; and determining a wiring between artificial neurons ofthe artificial neural network architecture based on the synapticconnections depicted in the 3D synaptic resolution image.
 8. The methodof claim 7, wherein determining the wiring between artificial neurons ofthe artificial neural network based on the synaptic connections depictedin the 3D synaptic resolution image comprises, for each of the pluralityof synaptic connections: mapping the synaptic connection to acorresponding connection between a corresponding pair of artificialneurons in the artificial neural network architecture.
 9. The method ofclaim 2, wherein the artificial neural network architecture is furtherparametrized by a set of weight values that are not derived from the 3Dsynaptic resolution image of the brain.
 10. The method of claim 2,further comprising training the artificial neural network using amachine learning training technique on a set of training data.
 11. Themethod of claim 2, wherein the network input comprises image data. 12.The method of claim 2, wherein the network output comprisesclassification data that specifies a respective score for each ofmultiple classes.
 13. The method of claim 2, wherein the biologicalorganism is an animal.
 14. The method of claim 13, wherein thebiological organism is a fly.
 15. The method of claim 2, wherein the 3Dsynaptic resolution image of the brain of the biological organism is anelectron microscope image.
 16. A system comprising: one or morecomputers; and one or more storage devices communicatively coupled tothe one or more computers, wherein the one or more storage devices storeinstructions that, when executed by the one or more computers, cause theone or more computers to perform operations comprising: obtaining athree-dimensional (3D) synaptic resolution image of at least a portionof a brain of a biological organism; processing the 3D synapticresolution image to generate data defining a set of weight values thatjointly parameterize an artificial neural network architecture;generating an artificial neural network that has the artificial neuralnetwork architecture and that is parametrized by the set of weightvalues derived from the 3D synaptic resolution image of the brain; andprocessing a network input using the artificial neural network, inaccordance with the set of weight values derived from the 3D synapticresolution image of the brain, to generate a network output.
 17. One ormore non-transitory computer storage media storing instructions thatwhen executed by one or more computers cause the one or more computersto perform operations comprising: obtaining a three-dimensional (3D)synaptic resolution image of at least a portion of a brain of abiological organism; processing the 3D synaptic resolution image togenerate data defining a set of weight values that jointly parameterizean artificial neural network architecture; generating an artificialneural network that has the artificial neural network architecture andthat is parametrized by the set of weight values derived from the 3Dsynaptic resolution image of the brain; and processing a network inputusing the artificial neural network, in accordance with the set ofweight values derived from the 3D synaptic resolution image of thebrain, to generate a network output.
 18. The non-transitory computerstorage media of claim 17, wherein processing the 3D synaptic resolutionimage to generate data defining the set of weight values that jointparametrize the artificial neural network architecture comprises:processing the 3D synaptic resolution image of the brain to identify aplurality of synaptic connections depicted in the 3D synaptic resolutionimage of the brain; and processing the 3D synaptic resolution image ofthe brain to generate a respective biological weight value for each ofthe plurality of synaptic connections depicted in the 3D synapticresolution image of the brain.
 19. The non-transitory computer storagemedia of claim 18, wherein each of the plurality of synaptic connectionsis between a respective first neuron and a respective second neuron inthe brain of the biological organism, and wherein processing the 3Dsynaptic resolution image of the brain to generate a respectivebiological weight value for each of the plurality of synapticconnections depicted in the 3D synaptic resolution image of the braincomprises, for each of the plurality of synaptic connections:determining: (i) a first tolerance region in the 3D synaptic resolutionimage around a first neuron corresponding to the synaptic connection,and (ii) a second tolerance region in the 3D synaptic resolution imagearound a second neuron corresponding to the synaptic connection; anddetermining the biological weight value for the synaptic connectionbased on an area of overlap between the first tolerance region and thesecond tolerance region.
 20. The non-transitory computer storage mediaof claim 17, wherein the operations further comprise processing the 3Dsynaptic resolution image of the brain to generate the artificial neuralnetwork architecture.
 21. The non-transitory computer storage media ofclaim 20, wherein processing the 3D synaptic resolution image of thebrain to generate the artificial neural network architecture comprises:processing the 3D synaptic resolution image of the brain to identify aplurality of biological neurons depicted in the 3D synaptic resolutionimage; and instantiating a respective artificial neuron in theartificial neural network architecture corresponding to each of theplurality of biological neurons.